{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "In this lab we'll look at:\n",
    "- How to build ROC curves\n",
    "- Use two different evaluation metrics to perform feature ranking\n",
    "- Compare/contrast the results of feature ranking on different evaluation measures\n",
    "- Build models on subsets of the features, using the different methods to select the subset\n",
    "- Compare these different models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import entropy\n",
    "import os\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we'll load the dataset and take a quick peak at its columns and size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['isbuyer', 'buy_freq', 'visit_freq', 'buy_interval', 'sv_interval',\n",
       "        'expected_time_buy', 'expected_time_visit', 'last_buy', 'last_visit',\n",
       "        'multiple_buy', 'multiple_visit', 'uniq_urls', 'num_checkins', 'y_buy'],\n",
       "       dtype='object'), (54584, 14))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load dataset\n",
    "cwd = os.getcwd()\n",
    "datadir = '/'.join(cwd.split('/')[0:-1]) + '/data/'\n",
    "f = datadir + 'ads_dataset_cut.txt'\n",
    "data = pd.read_csv(f, sep = '\\t')\n",
    "data.columns, data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next step we'll use the Decision Tree classifier's built in feature importance attribute to compute the normalized Mutual Information/Information Gain of each feature. Note a few things about this approach: 1). With extremely high dimensional data, one may want to calculate the normalized MI directly for each feature (the code to do that is a bit more complex so we used the DT instead), 2). The DT is a greedy algorithm, so the feature importance ranks it produces may not be equal to the rank of normalized MI calculated individually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#import the decision tree module from sklearn\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "#build a decision tree with max_depth = 20 using entropy\n",
    "Y = data['y_buy']\n",
    "X = data.drop('y_buy', 1)\n",
    "dt = DecisionTreeClassifier(criterion = 'entropy', max_depth = 5)\n",
    "dt.fit(X, Y)\n",
    "\n",
    "#Now use built in feature importance attribute to get MI of each feature and Y\n",
    "feature_mi = dt.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll add the feature importances to a dictionary where key-values are: {feature_name:dt_feature_importance}. This can be done in one line using the zip and dict functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Add features and their importances to a dictionary\n",
    "feature_mi_dict = dict(zip(X.columns.values, feature_mi))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to compute feature ranks using AUC. We can do this without fitting a model, by just seeing how well the individual feature ranks the positives and negatives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#define a function to print ROC curves. \n",
    "#It should take in only arrays/lists of predictions and outcomes\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "def plotUnivariateROC(preds, truth, label_string):\n",
    "    '''\n",
    "    preds is an nx1 array of predictions\n",
    "    truth is an nx1 array of truth labels\n",
    "    label_string is text to go into the plotting label\n",
    "    '''\n",
    "    #Student input code here\n",
    "    #1. call the roc_curve function to get the ROC X and Y values\n",
    "    fpr, tpr, thresholds = roc_curve(truth, preds)\n",
    "    #2. Input fpr and tpr into the auc function to get the AUC\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    \n",
    "    #we are doing this as a special case because we are sending unfitted predictions\n",
    "    #into the function\n",
    "    if roc_auc < 0.5:\n",
    "        fpr, tpr, thresholds = roc_curve(truth, -1 * preds)\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "\n",
    "    #chooses a random color for plotting\n",
    "    c = (np.random.rand(), np.random.rand(), np.random.rand())\n",
    "\n",
    "    #create a plot and set some options\n",
    "    plt.plot(fpr, tpr, color = c, label = label_string + ' (AUC = %0.3f)' % roc_auc)\n",
    "    \n",
    "\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.0])\n",
    "    plt.xlabel('FPR')\n",
    "    plt.ylabel('TPR')\n",
    "    plt.title('ROC')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    \n",
    "    return roc_auc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we'll run each feature through the above function to get its invdividual AUC and also plot on a chart. We add some extra lines of matplotlib code to control the formatting and position of the legend. We also want to add each to a dictionary of the format {feature_name:feature_auc}, similar to what we did above (though not using the same one liner). Take some time to review the chart and think about why different features produce differently shaped curves. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x117969e80>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jpMf7SZqsVA0morYuttmvZMDrBGBh0cyfX9/KzIVeOma5OLh+iMe/9CjV9AiKqqOY/Iz7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\nAqKFBDvig3Tn4vSUyvQpJgZ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Gi/w9FKdP0zTKT2yh8ssnUbetR69OkLB4idpbyDqXAmBt\nDtC+diWhNStxzz9xl+xkrMTzDw/x4pOjFPO1Q8tdXhN/9S9Lmb3Ed8bG/7kHt7Ft/G1lrL0qBk3n\n3tYWlt21BvUkunqPjIzQ2tp62DKjwcpLL71+xLqpapnNqRjbMwnKWp2Q2coNoXbmu3yYTuKYQpwr\nUz64UBTlBuC7gAG4U/8/7N13eFzlmfD/7znTm2ZGo1HvspplWxUbg7EBQ6gJIZAGm5AE0t60H9ls\nNnmzyaYs+6bthmw2GzbBgCGU0APBGDAYg8EB23K3LKv3NhpN73PO7w+BweCGLdmy/XyuiwvPzNGc\nI7A0536eu6jqz9/zejGwGnC8dcz3VFVdc9IvVBCEGTcUTrBlIsrmiQibxyMMRaaLHWsdBr4038WF\neVbmO41nZUcUXyzIbk8fO73j7I/F6NFqGTA5CBqsgAEyCrDGQxT4vSz19FEc11KpsVOXWUxBQQm2\nKiNaUZMizCAlkSL66OukHluLZt/rpNUAHkMmE7Y8wpo8AMwl+ZSuaJ6ekj2v+LC7DEpa5bW1w2x4\napBkYnr2xJQnjqqqNC7LZuGSLGQZJEli3kIHGU79Ua8vmVb4/JM7GA7Gj3icoqiMBOPU+qI0er2o\nigZDppvS5aXUL8ymLvvYOlMBBINBbDYbNtvBX/Pbn7/K3m1Rzlm8AJhePOmNBNnq99AVDiAB1VYH\nzY4sCowWkfoknFbmdHAhSZIG+D1wKTAIbJYk6SlVVd89kvJfgIdVVf2DJEnzgTVA6Um/WEEQToiq\nqgyGk2yZiLB5IsKWiQjDkenVSbtepjnLzM01mazIt5JjOns6DkWTMfZO9rNzcpR94RDd8nRx9aTJ\nAchgzcVgjFEQmKR+ZISiKJTLGdTZ8ynLr8FeZkZnnNO/6oXTWDoUI7r6JdLPvICu5+8kpCQeo4uJ\nzBKib90Q26pKKF/eTPbyFiylB7dkbd/uZccmD+rBs+vobvMz1B2irDaD4vzpjk72TD3nX5FPVu6R\n05BUVeWOLf2MhQ4OIiLJNG8O+VlcYCff9v66i+hUjKkuL6lomHwUlo3GaFxQTdPnF2HIOHrw8m53\n3nknX/ziFwFYuXIl69at44YbbuCOO+5gbDjNr/91MxkOPfF0mt1BL60+D95kHLNGy3mZOTTYXdi0\nH+ycgjBXzPVPnMVAp6qq3QCSJD0EXAO8O7hQgYy3/mwHhk/qFQqCcNz8iTTrhoJsHp8OJkaj08GE\nU6+h2W3ipiozLW4zlXbDGb87kUyn6JgaZKdnmLagn05UBoxWRs2Z08XVpiy0ege5oUkqJjxcGB2n\nXDVTY82jqqAMZ+1CDJazJ+gSTp3UeIDoqudQXngR3fAW4hoZjzELj7uGGNN1nPYF8yh4K6Aw5b9/\nSrZ3PMbjf+pk+2sT6I0a9PqD032sdh1f+H4djcvcx7xqr6gqbwz6GAzE+M2mHqx6zfvSiApsBv59\nZQ3FjukARUkp7HlsP13P7UZNeAAJjSWX6mvqqby85AOlPgHcdttt/Mu//MtBz+3ZsweA++77Mx1t\nUzz9SBcA9goj/9Ozh4SqkGc0c3VmMdVWB1qR+iSc5uZ6cFEAvHvm/SCw5D3H/Bh4XpKkbwAW4JKT\nc2mCIBwvVVV5diDIz7eP4Y2nyTRoOMdt5ma3mXPcZsoz9GdsMKEoCv2BUXZMDE4XVysp+gxmhs2Z\npDQ60DuQMjPIDnvJ8wdoGZ6kLG2k2pzD/LxisipqMNh0Ik1COKmSXWNE71yD+sp6dJ6dxHQGJow5\nTOYsIqEqIMs4G6opWdGC+4ImDFmHrn9QVZU31o3y6B0dpBWVqz9bxsqPFaH7ACl6h2tEs6HXy5ef\n3nXg8aOfbKbceeg5FqGJKK13bsOzuwNZjqIoOmyl1TR9vhHXvKNP+D6UH/3oR/zsZz878Fir1ZJM\nvjO3Yt8eL/91WysAhiIthmuNVFntNDnc5BlPfN6GIMwVc7pblCRJ1wOXq6p6y1uPPwMsUVX16+86\n5ttMfx//IUnSUmAVsEBVD95klSTpS8CXAIqLi5v7+vpO1rchCMK7DIYT/FvrGBtHw9Q5jfygMYeF\nmcYz8mZ5POxl+0Qfuyc9dKQS9OkMDFqcRHXvpHU4oz7y/T4KwwlKkzqqjC7qckrJy3dicohZEcKp\nE9/eS+yuZ5A2vYwusI+AzsqEOQ+v2UFSUaanZLfUTQ+1O7/xqFOyg/4ED/2unR2ve5i3wM4/fLv2\nqClO7/XzVzu5a9vgYV+XgPuvb8Bl0lN2iMBi4O+j7HpwGzHPALKsoEh2CpfW0vi5OnTHkW550003\nce+99x4IeCRJwmAwEIvFDhwTTafYGfCy9okeRp4PUPRxB+cvzmdxXg5m7Vxf4xVmgugWNbcMAUXv\nelz41nPvdjNwOYCqqpskSTICWcD4uw9SVfWPwB9huhXtbF2wIAiHllJU7uvw8j97PEiSxPcasvn0\nPCeaM+Dm2R8LsXuijx2TY+yPR+nVahkwOwgYbIABnAVYEmEK/F7OHRygJKmhUutgoauYoqJyLLVi\nVoRw6imKQmLDXuJ/fj8FDHQAACAASURBVBZ56wa00T4i+gzGzQX48s4hpSjTU7KXLHxnSrb12Fbc\n+zuC/OHHO4kGk3z05gou/mjRMbU2XtsxziN7Rw883jkaoMxp4uqqnEMeX+Yw0ZJ/8M5DOplmx/37\n6Ht5D1Lai6pI6O35zL++gfKLiw75Pkdz9dVX88wzzxx43NzczNatWw/aVRmPR9nqm2BvcIqUqhJu\nn64B+cali8jIEK1khTPXXA8uNgOVkiSVMR1UfAq44T3H9AMrgXskSaoFjMBhRnQKgnCyTURTbJ+M\n8Me2Sdp8cS7Ms/KDphzyzKdffUAsFWevp48d4yPsi4bokTUMmGx4zE5AgoxcDKk4eYFJFo6NUhwf\no0K2sdBZSHnBfDIqTcgakU8tzB1KKk3sr5tJPrIWze6NyMlxQoYMxi3F+B3nkFYUNBYTWUvryV7R\ngmvJwsNOyT6cSDDJnbftRquV+KfftlBQZj2mr3u1z8t/vdHLSDBOReZ0EFPiMPGZ+gKuqck96tf7\nh0K03rmNqf2dyHIcVTFgr5xP8xcasRcfe8end7vxxht54IEHDnrO5XKxdetWANKqyv6Qj1afh8FY\nGJ0kU2fLxDWm49FAAACrVRRqC2e2OR1cqKqakiTp68BzTLeZvUtV1T2SJP0U2KKq6lPAPwJ/kiTp\nVqaLuz+nzuVcL0E4gyUVlX2+GNsno+ycjLJjMnqg45PbqOU3S/O5pMA251N9UukUHd4hdowPsjfk\np1uCAaOFUXMmiqwBqxuNOZPckIdy7yQrhicol6zU2fOoya/AUVaPRieCCGFuUiJxog+9SurJ59F2\nbgIlgN/gZMJRTEBbjKIo6OxWcpY14V7ePD0lW3/4xQBVVZmaiJNOH/qj98lVnfgm43z7V43HHFgA\nfPVvu0ikVT61II+fXlx9zF/Xs2GAPQ/vIOkbRpIVJJ2TohUt1N8wH63h+Novt7e3U11dTTgcPvBc\nQUEBg4PTKVrhVJLt/km2+ycJpZM4dHouyspnoc1J2Jvi13dsxj8VZ8WHipDFLqVwhpvTNRezRUzo\nFoSZkVRUdnmjB1rHbvdEib51g5Fj0tLgMlHvMrHIZWK+w4B+jq3aTxdXj7FjtJ89gSm6lDR9RhPD\nlkySmunVRUlVyIpMURDwUxRJUaYaqbXksKCghKx8u5gVIZwW0r4w0bvXkV67Dl3fmyjE8RhceOxF\nBCQJVVWnp2Qvb8a9vBlHffURp2S/LRZJcf/t+9i28cgJA9feUsHKjxUf8/Um0wp1v3+FL7cU8+2l\nZUddkEjGUmxfvYfB1/YiqX4URcboKmLBp+opPi//iF97JBUVFXR3dwMQCASw2Wzceuut/OY3vwFg\nOBZmq8/DvqAPBZUys41mRxZl5gxkSeKVdYM8cGcbAOdfXMA/fLF2zi+uCDNP1FwIgiAwXSPR5oux\n5a3AoS+YPOh1FZWxaIrYW8FEpd3AtWV2mrPM1LtM5M6xtKeJiJdtw33snpqgM5WgT29g0OokojOD\n3gZZNhxRP/lBHxdN9lGm6Kg2uliUV0bevBr0JvHrUji9pIa8RO9ci/LSi+hHt6HIKpPGbDw5Cwii\ngKpizJ2eku1e3oy9ruKoU7IBopEUT67qomPnFJFginAoyWWfLCGn8ND1F1a7jtrmzA907fH0dE8W\nh1F7xJtxb7ef1ru2EejpRpYTqIqRzLpFNN/cgDXn+DswFRQUMDx8cGf7p556ihtvvJFf/cd/sDsw\nPZtiJB5BL8s0Olw02bPI1B88P2PbG2MA3PTVOubXu0RgIZwVxKelIAgHqKrKU30Bnh0IsM0TJZya\n/oAvseqocRh5727+BXlaWtxmmrNMOA1z49dJMB5m+2gPuybG2J+M0afTMmi24zNmgGwAVyHmRISC\nwCSLh4coTWmo0jtYlF1CaUklBuvcCooE4YNItA0RW7UGNr6Mfmo3iqxh3JTPZH4z4fT0AoG5KJvS\nFS3TU7IrDz8l+1B69/m5+xd78U7EWLgkC4NRw9LL8qhadOjWs8eqdcTP2o53dj9iqTTAIXc7FUWh\n87l+2p/cQSo8iiSpyIYsyi+pY8EnqpG1J7ZDKsvyQYXZF154IevXryeQTPCKZ4QdgUki6RQuvYFL\n3YXUZTgxyIfe5RkaCAGwdMXx754IwulmbtwNCIJwyvUFE/x46yibJyKUWHVcVZxBi9tMi9tE9hyc\niB1LJdg73sv2sWHao2F6tTIDZhsT5kxAhsw89OkEeQEPdZ5xiuMTVOpsLHIVMa+wHHO1QawiCmeE\n+BudxO75G9KbG9CHOklrDIybC/EWLCGSSgBgK8+fnpK94v1Tso/Vmy+N8sBv27Fn6rn1l02Uz7fP\n2Pfw/XX76PdFMeneuUl3GnXUZL1ToxEPJdh29y6G39yHTBBF0WDOKWXRPzSS35R9Que32WxEIhHS\n6TR//OMf+eIXv8g111zDE088wUA0zBMjPXSE/ADMs2TQ5HBTYrIe8XeIqqoEfAksYsFCOMuI4EIQ\nznJJReWedi9/2OvBoJH4cXMuHyuzz5khdmklTYdniG0j/bRFAvRIKgMmC6MWF2lZA/ZsNLY0OaFJ\nSn1TLB+bpEK2ssCRx/yiSjIq6kWbV+GMoigK8bU7SDz4LPLOV9HFBkhpTYxby/AWLiWWnA4o7NXF\nzFveTPaKZkz5x3/zrSgqT6/u5oVH+qlc5ODm/7sAa8bM3TCPh+P0TEW5qNTF/35k4ften2jzsu2e\nbYQGe5DlFChmshobafpCPeZM4yHe8dgZjUbi8fiBx8FgkFtuuYXPfuHz7A1OcXd/OxOJGEZZw2Jn\nNo12F3bdsXXLGuyb3rVY0JB1QtcoCKcbEVwIwllslzfKv24ZZb8/zqUFNv5vYw7uU1RboCgKg/4x\nWof6aAv66CJFv9HEkCWThNYAtkywZZIV9lIQDNA06accI/NtOSwsLCOrfIEIIoQzlpJIEX10E8nH\nn0Pb9hra1ARxnZUJWzlT7mLiiTjIEs4FFRSvaMZ9QTNG94mlKu3b5uW+/2wjOJVEUVTOvyKfj3+l\nEu0Md0L76IPTDVY+sSDvwHOKorDvr910PLMLJTZdt6Ax5VB51UJqrilHPobakCOxWq0HdX4C+O53\nv0vKoOeliSF2BrzElTTZBhNXZBdRa3Oi+4DnDAamg7wly/OOcqQgnFlEcCEIZ6G2qRgPd/t4rNtH\nllHLb88rYGXB8fV9Px6esI+tA93s9k3QpSTp1+sZtDoJ6y1gzgBzBvZYgPygjwv9/ZQremosLurz\ny8ivrTnhnGpBOB2kI3Gi964n9fQL6Lo3oVECBHUOxp3l+IyVJBOJ6SnZjdWUr2gma1kjekfGCZ1z\noDPI2od6SSUV2rZOkVNkZskleeSXWmhenj1jqYQjwRg/f7WLeFrBE0lSYDOwvCSTiDdG6107GN++\nH1kKoyharIWV1N/USM78D1YU/l7BYJDW1lZWrFhxUGDxy1/9iuu++iVafR7+2NeGDFRZHTQ7sigw\nWo77ex4ZnN65sNpEWpRwdhHBhSCcJfyJNGv6Azze46PNF0cvS3yiwsG3Frqx6WannWowFmb7YDc7\nveN0JqPTxdUWO1MmO+iNkF2EKRmlIDjJOWMjlKY1VBucNOQVU1ZZI2ZFCGedlCdIdNVzKM+/iG5o\nCxo1RkCfyUR2DT6tllQyMT0le/FCslc041paj85mmbHzP3lXFz37AmTnm1i8Mofrv1yJ0Xz8twrR\nZJo1HeMk3zMDY8uwj2c7J6jJsrAwx8bX8928+E8vEBnrR5bTINnIXXwOjTctxJBxYkPngsEgdrv9\nQJG2qqoMDQ3x9JpnaL7+o2zzeXhkuBuLRst5mTk02LOwaU8sINi320vnPh8AjhNM3RKE040ILgTh\nDJZSVLZ6Ijze4+eFwSAJRaXWYeAHjTlcWZyBfYZmNMSSCXYP97JjfJj9iRB9GpkBi40JsxNV1kBW\nHrp0grzgJLVeDyUpD1W6DOrdRVQXV6KfgwXjgnCyJHvGid75LOqGl9BP7ECDgs+YjSevAb+skk6m\n0Jj0ZJ3XQPbyZlxLFqExfbAp2ceivyNI+/Yprvl8OZd+vGRG3vPxtlF+8nLHIV+rcJr5N8lG93O7\n8Sc3o6oSOmsu1R+tZ95lxSec+jQ8PExBQcH7nvfEY+zWKkQuXMKLE0PkG818OLOEapsdjTQzCxr/\n/YttpJIKFqsOi0XcaglnF/E3XhDOELG0Qoc/TttUjDbf9L87/HHiikqGTua6MjsfK3NQ6zz+VbS0\nkmb/6ADbRgbYFw3Qq4EBk5kRq4u0rAVXNrLiIifspTjg5wKPj3kaCwsz81hYUoO5UqzgCQJAfGcf\n8VVrYNN69P42NJKEx1TIZNG5+JUUSiqFzmog+/xGslc0k9lcd8Qp2TNh3WP9GM0all35/hvy47V1\n2E+2Rc/jn2o+8Fx4MsbO+3cTfbONbimKoujIKKuh6fONZFbMXAeq9wYWD734PGpVGav696GRJGqt\nDpocbvKMxz8P41AURSWVVLj06hKuvr4CjUjjFM4yIrgQhNNYIJHmqT4/f+31s98f5+3MgwydTK3T\nyKfmOal3GVmeZ8X4AaZjK4rCgHec1sFe2sJTdJNmwGRkyOoirjWA0wVOF67IFIUhPw2+XipkIwsy\ncqgvrcA57/0dXwThbBd7eQ/xPz+LvPUV9JFuZEkz3eGp5AICyRhqWkFvM5J3QRPZK1qOeUr2TOja\n42PbxnFWfqwY0yFW2jsmw/zzC20k3pPedDR9vigXl7nIthjo3zTC7ge3E5scQJYVDLKdgvMbaPjs\nfHQzsHu5YcMGLrzwQsrLy+nq6sJut+P3+3lo69/xOq30ppLYknGWu/Koz3Bh1s7OLVA0kgLAbNFh\nMJ6c/3+CMJeI4EIQTkP7fDEe6pzimf4A0bTKwkwjN9e4qHUYqXUaKDDrjrkIcdw/RetAN3v9HrrU\nBP1GPYPWTEJ6C2RkQEYGGfHgdHH16ABlqp5aq4vGojJyy2rFrAhBOAxFUYg9tZnEX55Du3sjusQw\nsqxjzFaJt+xCgrEwKCpGh4mi5RfgXtFyzFOyZ8LESJS/Pz9CPJpm47PDZOWaWHR5Dv/5ejdp9eAg\nYs94iN3jIS6tyOKD/MSXZhg5dyjBE5/7C1J6ClWR0DsKmH99PeUXFc3I9/HYY49x/fXXH3jc3d3N\nWCzCA+07aQtO0auqFOsMXOwuoNIy+222B/uCs/r+gjDXieBCEE4TibTC84NBHurysX0yilEjcWVx\nBp+scFJ3DKlOwWiY1t4udk+N05mO0q/XMmi14zU5wGwCcxHGZIyC0CTNE6OUKzI15kwa8ksoK60V\nbV4F4RgosSTRh14l+cRz6Do2oU17QTYy7Kxmyl5FKBIGVcWcZaV0xYW4l7dgqyo5aUH6aH+YgC/O\nxnYvG9YMkUopaDQS+Y1WLryhlLvahnh4zwiGQ+x0nlvo4PdXLTim8/gHQ2xd1YpvfxeyHEdRDDiq\n6mi6uRF7ofXob3CM3vvfTaPV8oc9W7lnYD86SWZBRiZN9izcBtOMnfNouvZPF3KXV81cipcgnE5E\ncCEIc9xwOMnD3T4e7/Hhjacptur4bn0215TaD1mQHU8k2dnfzc6JYTqSIXp1MoMWG+OWTFSDFnLz\n0aaT5IUmqfFNUurxUmnIoDG7kOryarSz1DlKEM5UaV+Y6OoXSa9Zh67vTbRqiJjGyqirhilbPZHQ\n9Eq2NT+T8uWX4l7RgrV05uoajoWqqqx9sI9n/tzDWDbsaJKgETiwDxHi8bW7AajNsvLXG1qO6zzd\n6wfY+8h2kv5hJFlF0mVSfOFiFn26Bq1hZn633Hnnndxyyy0HPac3mbhtyyukdVoUnZaL7VkszMjE\nqDn5tznD/dMtaEtnsH5EEE4nIrgQhDlIUVU2jYV5qMvHhuHpD6oL8618ap6Tc7PNh9zWn+ju4pGd\nL/A/85dM10Xk5CCpbnLCXorCQZb5/FTqrCxy5bGwpBaj4cTaOwrC2Sw1MkX0zrUoL76EfqQVLXGi\nOidDeQvxmY1Eg9MBRUZJNvOWX4F7eQvmguOfkn08lLTKA3/u4Bfjw6S00wGG5jIJRQa7Vstvr5p/\nyN8lZc4PVuCcjCbZvnoPA6+3Iat+FEXGmFXMwk83UnRu7kx9O9x6663cfvvtAHzta1+j2+flJ48+\ngGnRfBRUSsw2mhxuys22U5quaTBq0GgkUW8hnLVEcCEIc8ya/gD/vWeC/lCSTIOGW2pdfLzcQZ75\n0AWPQzt30fv6fYzperhj2T+TG41wbchDnSOHptJyMizHlsYgCMKRJTtGiP7pGdRX12Pw7kZHGp8+\nh4Gixfj0GuKhEKgpHPMKKFregvuCJozZJzb47Vhs6J3ksb2jBz2XSin0tQeYiCSIOyUWay24MvTk\nlUwPhVtS6OC84hO7Nm+Xn9a7Wgn0diPLSVTFROaCRTTd0ojVPXNpSDfddBP33nvvQc9llpXw58EO\nbA11LMpw0WjPIlM/8+15j0cqqeJ0ic54wtlLBBeCMEcEEmlu2zbGM/0B5juN/HKJm0sKrOgPkfus\nqiq9b7zJ4Nb7kdlFHAt3nPcDtDoL95QvolAndiUEYSbE3+wkds8zSG++gj64Hz0qXlMR/aUX4JNV\nEuEwkpIgs24+ZStacJ/fiN55YlOyj2Y0FOe1fu+Bx6taBxjyx3C+KwUoHEiSTqlkZBuoz7dyx4cX\nopmBuilFUeh8ro99T+4kHR4FVGRDFhWXLaDuuirkGW672t7eflBgkVVeynfWPoFLb6TZnkVdhhO9\nPLd2CNp2T2I+zGKQIJwNRHAhCHPA1okI33tzmPFoiq/XZXFLjQvtIW4E0qk0na9sYGz3Q2g0HaCY\nseRdz5qlV9ETj3BHTqEILAThBCiKQuKFncQfWIu8/RX0sX70SHisFXjnXYw/nSAZiSKn47haFuJe\n0ULWDE/JPhxVVZmMJvnx+v281DN50Gvz9quUd6cPPLY59dz8/TrmLXDMyLnjgQSt9+xiZHMbMiEU\nRYM5t4xFn2kkv8E9I+d42wUXXMDGjRsZHBzEWJSPVq8nt7qSbzz2ZyotdpocWRSbrHOyU53fFyfg\nS8zJaxOEk0UEF4JwCiUVlf/Z42HVvkkKLDruu6iERa73pxMkY0na1z3HZNcjaLX9SHIG9uLPUH3p\nJ3k2leLh8SG+aHex0mI7Bd+FIJzelFSa2GN/J/HoWrR7N6JLTaBFxuOoxVvyIfyxMOlYHE06TtZ5\nDbiXN5M1S1Oyj+QXG7u4a9sgANfPz+UqeyZP/KkT32ScD3+4mGU/zEd6q0Db6tChn4EC6rG9Xrbf\n00p4qBdZToFqwd3YRNPNizA5Zjb1Z8GCBezZs+fA44WLF/PPLz/Df+zdQr3dRYM9C/scXzx56dl+\nAD7y8YpTfCWCcOqI4EIQToGkovL8YIC7273s88W5ttTO9xqysbynU1MsEKPt+acJDD6OVjeKRusi\nq/rLzFtxLRqdgc5EnB9O9NNiNPPtzJNbLCoIp7N0JE70vvWknn4BXdff0Sp+tJKOcdcCfNnN+IN+\nlEQSrRIn++LFZC9vxtk8H80paoSgqirPdkxQZTGR3augezrAQ50juHKM/J+fNVE+f+Y6EymKQtuT\nXXSu2YUSGwdAY86h8qqF1HykHHmG53C8u1D7bbUrL+S7d91BsyOLGqsT3Uma/XEigoEEz/21F4Dm\n83JO7cUIwikkggtBOIlGI0ke6fbxaLePybfayv5maT6XFh6cox0YD9D+/BNEJp9Gq51Eq88jr/5W\nypZehfRWfnFEUfjG2CBGSeY32QVoxTa8IBxRejJI5K4XUJ5bh25wMzo1iiKZGc1ZiM+Vid/nRU2l\n0atJ8q68gOzlzTgaqpFnaZLzsZqKJvnT1n5GQnHc7RIlcQM5hUYaPpbJlTeUYjDNzPVFvDFaV+1g\nfHs7shxBUbRYiyppvKkRd+3MF6b/6Ec/4ic/+QkXf+JjB4KLhmuu4id/+G+aHG4KjObTKr2oc9/0\nfIuahZkYjeL2Sjh7ib/9gnASbJmI8EDnFC8OBVFUWJ5n4dPznJyXY0GWJFQlTWi8ncBEhP43t5AM\nPYtGG0BnLKFo8VcpbLgISXpn5U5VVf7VM0JXMs7ducXkakXxoCAcSrLfQ/TOZ1HXv4h+fAd6kkQ1\ndoYKmvE5Mwh4PKCkMWoUCj92CdnLm7HXzUM6RCOFk0FVVYZ6QuyZCJFUpqdkvzzq5akBD8YU5Psk\nvnl7A073zKUkDbeOs+PP24iO9SPLaZBt5C5eTOPnF2CwzvxOTWZmJlNTUwCMFWZTftEyfrDub1ze\n0EyD3YX1NPx9pqoq+3ZPF9mLlCjhbCeCC0GYRRPRFP9v+xjPDwax62U+W5XJJ8odFL3rA1tRFHY+\n+n8JT7x54Dm9pYayZd8ju/rcQ67cPRz08WTIzzedbs43z9y0W0E4EyR29RNb9Qxs2oDetxcDCmFd\nNv1ly/DbTATHJyAZw2xwUHLDlWSvOLlTsg8n6E/w0O/aeb7TMz3k7l0cXpXFb8ItP6ydkcBCSSns\nfrid7nW7ITmJqkrobLnUXFtPxaXFM576BGC1WgmHwwc9Fx6f4MO5JVTPW4RGmvupT4cz0Btkw/MD\nAGTnfrA5IYJwphHBhSDMAlVVeazHz3/sHCeeVvnGgixuqsrE+K7VUFVRGdrlpWPDc+g1bxKNXEh2\n9RKKm0pxFNYc9r33xmP8dHKUZSYLX3NknYxvRxDmvPirbcTuXYO89RX04S4MQMBYxHDlSnwmLeGx\nCYgEseY7KLvqo2SvaMFSmn9KAopH9ozwy41dqO96Lp1SSMQVVBPQJGOUJb5bU3xgfnapxUjpTVYK\nyk5sMSE0FmHrnduZ3LsfWY6hKHrsZTU0fqGRzPLZmSitqCpZuTkHBRbXf/sb/O62n5NrPDNuxG//\nt60AfPyz1Vgz5nbRuSDMNhFcCMIM6w0m+PHWEbZMRGlxm/hxcx6ltnc+bNJJhb4tE+x/ZYTQZJCc\nokfRGgtZccv30R2lWDSopPnG2ABOWcOvswsOOV1XEM4GiqIQf6aVxINr0OzaiC4xjAHwWysZmH8F\nfo1KZNwDgSkyCsuZd+1FuJc3Yy48eYW2r/Z5eXLf6Puef2PQh82g5eIyFyrQucvHUHcIi11HbVMm\n1gwdDXkZXF01c9fa//owux/aTmxyEFlWkDR2CpY10vDZOnSzUB8QDAZxuVwkUyn+p2sX31r3FD9p\nXMYtP/gut//op5g1Z87tRzSSJBJOodVKXHBJwam+HEE45c6cn25BOMViaYXV+738795JjBqJHzfn\n8rEy+4EAIBFN0fX6GJ0bR4mHUjiLLFQv205gYIqaK/71qIGFqqp8b3yYoVSS+/NLcZ1BH86CcCyU\nWJLoXzaSevJ5tO2vo01PokdmyjGfqepmfEqcmMcLUx4ci6oo/PRluJc1YcxxnfRrDcVTfO2Z3Wgk\niSzzwT/bZp2Gb55bytVVObz81CBjawb58kcK+OjN89DpZi41KBVPs+P+vfS/shcpPYWqyBgc+cz/\nRD1lK4pm7DzvFgwGcbpcpJPJA8/5Onr49HnL+GEyeUYuiHR3+AG4+uMV6PVza6CfIJwK4u5EEE5Q\nPK3waLePO/d5mYiluKzQxvcbc8h6azUw6k+w/5URet4YJxVXyKmyU31RHmbHJDsfeQp39aXY8+uP\nep7VAS/PR4J8LzOH5jMklUAQjiYdiBJd/SKpZ15A3/cGWiWEhA5vdj1ThRfgiwZJeP1Ik+M4m2sp\n/fxHcC9rmvUp2Ufz32/0Eksp/PC8Cq6rzj3kMV17fDx5ZycLFru4/suVM5ai5e8PsnXVNnydnchy\nAlUx4KheQNPNDdgLZqdGK60quNzZ+CffNdxPkvjf+1bzpQ9dPSvnnCui4RQApRWzk1YmCKcbEVwI\nwnFKpBUe7/Hzp32TjEVTtGSZ+MWSPBZnT0/qDYxF2b9hhL5WD6gqhfUuqi/Mw5FvQVUVtt77ZTQ6\nE6XnffWo59oWi/CLyTEuMdv4gn3mW0IKwlySGvURXfUcyrp16Idb0REHyYwnvwFffi4+n5dkIIQ8\nMUbm4gVkr2gh67yGkzIl+2jSaYUnn+rnroFBzGGVv/+okzfoPOzxTreBf/h27YwEFl0v9tP22A6S\n/mEkWUXWZ1JyUR2LPl2DRjc7K+odA/388D9/zbJvfAlnecl0cCFJ/PWZv/GRK66clXPONRrtW4ML\nbadflytBmA0iuBCEDyipqDze4+NPbZOMRlM0ukzcdk4eS7Kne7J7eoO0rx9mZK8PjU6m/Nxsqpbn\nYsmc7vCiKml2PvZ14qExypZ9Db3ZecTzTaVTfGtskFytjl+4T00BqiDMtmTHKNFVa1BfWY9hchd6\nUiRkO2Ol5+HPdeGbGCcViqCZGCfrvHrcy5txLVmE1jyzU6I/CL83zoO/ayfkTxKRFda7Y8QUhSgK\nWCRuLs1nQe2RdxkXLsnCmnH8N6XJaJJt9+xmcFMbshpAUWSMWcUsurGRwsWH3jE5UaqqsvaN17lq\n2QrUdBqA677z//Hi+vUYfEEKCs6uuoN0ero0X9aI382CACK4EIRj5o2nWDsQ5O72SUYiKepdJn56\nTh5Ls82gwkibj/aXR5jsCaI3a6m9tIB55+VgsL5z4+Ab2MpU/2ZC4/uQNXpyF3zkiOdUVJV/Gh/G\nk07zcEEpGRqRzyucOeJbu4nd/QzSGxvQB9oxoBLTZjNctRJ/Vgb+oWHS0SBaj4L7gibcy5vJbKk7\nZVOy35ZIK9y3fZCXnx1iaipOZraRcV2KIUOa4oSWApeZlnlOvnHBvFm7hslOH613bSPY140sJ0Ex\nkbmwnqabG7C6TbNyzqSicM+zf+PL11yLmlYOeu3jBeXTf7Cc2nS0U+Ht4EIjggtBAERwIQhHFEik\neXEoyLMDQd4YD5NWYVGmkR8353JejgU1rdK31cP+l0cIjEUxO/TUX1NC2WI32kMU9nW/8luivgEk\nWUPtVf+OrDnyFzRRYgAAIABJREFUTdL/+jxsiIb4cVYuCwyzc8MgCCeLoigkXtxN/P41yNteRR/r\nwwhETCUMzb8Sf6YZf+8Ain8SvSZJ7mXn4V7ejLOx5qRNyfZEEoyG4kc85s1BH794rRsymP6HGAAr\ny1z84cMLZ+3aFEWhY00v7U/tJB0ZA1Rko5uKD9VRd10VsnZ25kT4kwm2+T3s9E/yrauvOfC8JEkM\nDg6Sn58/K+c9XcQi0zUXmlM0eFEQ5hoRXAjCe0RSCi8Ph3h2IMDG0TBJRaXQouPz1S6uKLJRZTeQ\nTih0vDpKxyujRP0J7LkmzvlUBUUNmciH+YBRVYVUPIS76hIqL/n+QRO3D+Xv0TC3T01wtSWDG2xH\nTp0ShLlKSaWJPfEGyUfWotn7GrrkGAYkwrYqRmuvJWDR4u/uR50cw6h1UfDRi3Evb8axoPKkT8lO\nphWueWALE5HEUY/VJVT+UcrhpltrDzwnz9LCdTyQYOtdOxndsg9ZCqEoGix55dR/poHcevesnFNV\nVfqjIW6/dzW3/59vYnbY+fPuVq7++PU88+hj+P1+bDbbrJz7dLP5tel2wwaj2FkWBBDBhSAA021k\nXx0Js3YgwIaRELG0So5Jyw3zHFxelMECpxFJkoiFkux5bpCu18dIRtNkldtour6M3Gr7UWshAsM7\nSUancBQvPmpgMZ5Kcuv4IKU6Pf8m6iyE04wSiRO9/xVSTz2PtnMTWsWHjIZA5gJ8Fcvx6xUCXf0w\nPICpMIfiT10+PSW7uvSk/11XVZVbntrF3vEgaVXFF0tRP6LF7FcOeXw8piDLcMkFBXzm0xVoZiui\nAMZ2T7J9dSvh4T5kOQVYcDc20/SFhZgcs1NrklDS7AlM8V93reKe73wf1OmUn8iUj2vzy7j24Udm\n5byns852H1qdjE0MzxMEQAQXwlksqahsGgvz7ECAl4ZChFMKmQYNHy21c0VRBo1ZpgM92UOTMfZv\nGKF38wRKWiW/zkn1Rfm4io+trWM8NEH7cz9F1hpxlS874rEpVeXW8SFCisLqvBIssthqF+a+tDdE\n9O4XSK9dh25gM1o1goyeQE4jvnnl+JUYwe4B6O/BWlFE2eeumZ6SXVYwawFFMq3wn5t6mIomD3OE\nyshYlE3eAJWKHkNMwjmi0qTqKZt/6LaiRrOWFR8uwOmenZt7RVHY+3gnXWt3ocQmANCac6j88CKq\nry5DnqXfB95EnFb/BLsCXv6xqhFVeSe40mq1JJOH+294dhsdmp46XlQidnEE4W0iuBDOKoqqsmUi\nwpr+AC8MBfEnFDJ0Mh8qtHFFcQaL3Wa071qJnBoM0/7yMIM7vciyRHFzFtUr8rBlf7D6h5Gdj5OM\nTpEz/2o0uiN/7X9NTfBmLMIv3flU6U9dJxxBOJpkv4foqrWo619CP7YdHQkkyYK/6Bz85UX4IgHC\nfcPQ2UFGbTkVX/442cubMRfNThej93p0xzCrWgfI1GnfN7xNVVXi0TSppEJmUmJRp4pBljnv8nJW\nXld00vPnI5NRtq7awcSO/chyBEXRYSuuouFzDbirZ6f9tKqqdEeCbPVNsOo3v2He0iVcfv7yA4GF\nyWQiEonMyrnPBJ7xKM881g3AyquKT/HVCMLcIYIL4awwFk3y114/j/f4GQwnMWkkLi6wcUVRBufn\nWtC9K6BQVZXxjgDtLw8z3hFAa9RQtSKPymW5mOzHt+09tncNACVLv3jE416OBPmDz8MnbA6utTmO\n61yCMJsSe4eIrvob0msvo5/agwGFpMaJd96F+Evy8HkniA6Pw752HIuqqLrmRtwXzN6UbFVVScYP\nTmGKhFNseGqQ3/UNYNGrNG9Mcqi9kYxMPVfeUMrSD+WhmaVi6KMZ2jrOzj9vIzrejyynQbaRu2Qx\njZ9bgME6O2k2sXSKXQEvrX4PD/7i12y4424Anuf3/F5VCQQCop7iGNz/p7207fJiMmtpWpJzqi9H\nEOYMEVwIZ6ykovLqSIjHeny8OhJGAc5xm/l6XRYrC2yY3nMzoSoqg7u87H95hKnBMEabjgVXFlFx\nbjY604n9qMhaPY7sFnTGw7dpHE4l+afxYWr0Bn7oOjkru4JwLOKvtxNbvQZp8wYM4U6MQEKfx8T8\nKwkUZTE1NER8wovU5sfZVEvJDVeStawRQ+bsTiyOR1P87gc76N0XeN9rkgTKpVqacm384IZDt4TN\nyjOhN5z8Itx0Ms3uv+yn58XdkJpEVSV0GXnUXLuIyg+Vztp5J+JRWn0e9gSnePAHP2HLw48f9Hp2\ndjaACCzeQ1VVfFNxhgdCDPWHGB4IMTwYYrAvRHmVna9+pwF5FmtvBOF0I4ILYU6LpxX+e4+HXd7o\nB/7a3mACTyyN26jlCzUuPlZmp/gQK4HppELv5gn2bxgh7I1jzTLSdH0ZJU1ZaHQnvpqZiodIhD3k\nLrjmsMckVJVvjg2SVFV+l1OIUdRZCKeQoijEn91G4sFnkXe+ij4+hBGImcsYa7qOQJ4db1cvyclR\n5KCHzHMWUvHF68k6/+RNyVYUldW/bqNvf4DLPlmC0fxOkCBrJOrOcXHF31qpyLeSX3pstVGzLTga\nZuud25ls60Ajx1AUPfbyWppubsRZOjvzIRRVpSPsp9XnoT8aQitJ1NqcBwUWhYWFDAwMzMr5Tzfh\nUHI6iBgIMfyuQCISTh04xu7Uk19o5aLLilh8fq4o5BaE9xDBhTBn9QYT/OOmIdr9cRpcJj5o1kJz\nlpkPl2SwLNd6UB3F2xKRFF2bxujcOEo8lMJZZGHR1cXk1zmRZnAVaqLjRQDMmSWHPeaXk2PsiEf5\nXXYhpTrDjJ1bEI6VkkgRffg1Uk88h3bf62hTHgxIxOy1jDYuJ+A2493bQWqoD43XiGvpIrKXt+A6\nd3anZMdjaTavHyMaOrigeLg3zM5NHq7/ciXnX53PX3aPEE2lD7y+f9RDOJkmZ5ZSiz6IvteG2f3Q\nNhLeISRZQdY6KFzWRP1n5qMzzs7HcCSVYkdgkm1+D8FUkjs/9xU6X3+Df/jc5/jHu+/GarVSWFhI\nW1vbrJx/rovH0owMvRU8vL0jMRjCP/VOG2KTWUtBkZWWpbnkF1kpKLaSV2jBajv1f6cEYS4TwYUw\nJ63pD/DjraPoZPj9skJW5M3cymPEF6fj1VF63hgnFVfIqbZTfVE+7nLbrHStebvewuQ8dHCxNhRg\ndcDLTRmZXG49+6bbCqdOOhQjuvol0s+8gK7n72iVIDJaoln1jM+/kmCGFu/udtI9HWg9FtzLGnEv\nbyHznNmbkh30Jwi+dYPX2x7gb/f1EPAeeu5E/YdzyTvfwX07hvj5xq73va6RoNZ9alJ8UvE0O/68\nl/5X9iApPlRFRu8soO4TDZQuL5i1847GImz1TdAW8pFWVX5/7Y0M7Nl74PX7V6/mvrvvJhgMzto1\nzCWplMLYSORAEPF2IDE5EX27yy46nUxekYXaha7pIKLISn6xFYfTINqAC8JxEMGFMKfE0go/3zbG\noz1+Gl0mfnluPnlm3Yy8d2AsQvv6Efq3TQIqRfUuqi7Mx5FvnpH3PxxVSeEoasHsfH83kb5kgu9P\nDFNvMPFdlygIFGZfajxA9M61KOteRD+8FZ0aQyMZieS34K+rJqBLM7VzH0r7XnTODHIvXYp7eQvO\nptmdkj05FuX5h/v5+wsjpFPqgedLazK4+ft1FFUcHCTsmgjy6Se286sHxgAozDDytxvPOahwWyNJ\n6E9yofZUX4Btq7bj6+pElhOoihFnzQKabm4gI3920rPSqkJ70M9W/wTDsQg6SUbfO8S3Lr3qoONW\nrlzJunXrZuUaTjVFUZkcjzI0+E4QMdwfYnQkgpKe/vskyxI5+WZKyjNYuiKf/CIr+UUW3DlmUTMh\nCDNIBBfCnNEdiPOPfx+mwx/n5ppMvl7nPqiL0/Hy9ARpf3mYkb0+NDqZiqXZVC7Pw5I5++lH4+0v\nEJnsxlmy5H2vxRSFr48NoJUk/iunEL1YIRNmSbJrjOida1BfWY/esxM9KVJyBuGyZfjnV+BPRfDt\n3I+6YweG7EwKPnIR7hUtJ21K9hsvjnL/7fuQJTjvsjwqFzmRJLDYdFQuciBJEsm0wjfX7GHvRAiA\nUCKFRafhtpXVyBLUuK2YdaduQnLXun72PraDVHAYSVKR9ZmUXryAhZ+qRjNL1xVMJdnu97DDP0k4\nncKpM3DvZ75E62ubMMxbxLfeOu66667j0UcfnZVrONlUVcU/FWd4IMzwW4HE0ECIkcEQiXd1DXO5\njRQUW1nU7H4riLCSk29BNwN1dIIgHJkILoRTLpFWeKzHz3/uHMeokbnjgkKW5Z7YCp+qqIy0+Whf\nP8xkXwi9Wcv8SwuoOD8Hg2VmdkKOfg1pOtb9OwA586963+s/mxxlXyLOn3KLyNeenGsSzh7x1h5i\n96xB2vQy+sA+DKgktVkEay8jUFOKL+jFv7sTNm/FVJBD8Scvm56SXVN2UlNBgr4Ef/nf/Ywt0VPa\n5GCPEfYkp6ZfnATWjwMwFkqwvneSy+e5seinb9bPL3ZyZVX2SbvW90qEk2xbvZuhv7chqwEURcbk\nLmHhjQ0UnjM7Hd9UVWUoFmarz8P+kA8FqLBk8NmqhUTD0zMpaubNo6enB1VVj/xmc9zbxdVvF1VP\n/zlM+F31Nxl2PflFVpZdXDidzlQ0XRdhPMEOf4IgHD/x0yecMrG0wmPdPu5q9zIWTbHYbeb/Lckj\nx3T8N9pKSqF/2yT7N4wQGItiduppuKaE0sVutPqTu6o5sOU+AGy5dZjsB+dYPxH08XDQx1ccLi40\ni7aPwolTFIXEhr3E71uD3PoK+mjvdMtYQyH+xusIVBUwNTpMcF8PvD6GtbyQsps+Mj0lu7zwpOeW\n750IMhKMs/6vA+wtUOjOSNA3NMWRNiuvn5/LbSurT3kevKfDx7a7Wgn29yDLSVTFhGtRA01fqMfi\n/mADNo9VUlFoC06x1e9hPB7FIGuo1Vu4trKO1HumZ3/mM5+ZlWuYLYl4mpGh8EFtXocHQvi88QPH\nGE1a8ossNC3JPrATkV9kFZ2aBGEOEsGFcNJFUgqPdPu4u30STyxNc5aJn52Tx9Js83HfNCRjaXre\nGKfj1VGi/gT2PDOLP11BYX0m8kmetAsQC4wysHk1AAs+evtBr3UkYvyrZ4TFRjPfcp66VVfh9Kek\n0sT+upnkI2vR7N6ILjk63TLWWol36Y2EyrPxdvcQ6u6F4V4yasqo+NL1ZK9omfUp2Yqqohxm5Xwy\nkuTjf2klqaigAcqgzm3l8U81n/LA4XAURWH/M73sf3on6cgoABqjm4rLFzL/2nnIs1Tb4U8m2PZW\n6lNMSZOlN3JZdiHzbU4+cuVVBwUWv/rVr/jOd74zK9cxE9IphbHRyIF6iLfrIzxj7xRXa3UyeQUW\nqusyKSi2kl84HUQ4XaK4WhBOFyK4EE6acDLNX7p83LPfizeeZkm2mV+dm8U57uMvqI4Fk3RuHKVr\n0xjJaBp3hY3m68vIqbaf0g8iJRUDoPyCbyJr3vkxCysKXx8bxCLL/Ca7AK34sBQ+ICUSJ/rQq6Se\nfB5t5ya06SlkNMScdQQariBY5GRyTzvR7nbo2Y9jYSWV37iB7OXNszYl+70G/FE+8sAWwsn0EY87\nf58GsyJxyw8WUJFtmZM3jzF/gta7djC6tR1ZCqEoWiz5FdR/tpHchVmzck5VVemLhmj1TdAZnh4Q\nWGW1U6U1UZedd+CYtWvXotVqWb16NTfeeOOsXMvxUBQVryf2vjavo0Nh0m8VV0sS5ORZKCqxsWRZ\n3oEuTe5cUVwtCKc7EVwIJ8WLQ0F+tGUEf0Lh/BwLX57voinr+IOKkCfG/g0j9G6ZQEmrFNQ5qboo\nH1fx3BiW9Xaus87kOOi5H3qG6U0mWJ1XQraosxCOUdobInrPOtJr16Hr34xWDSOjJ57TgL+pmUC2\nEU/rHuJ7dyG1a3A21lD8yctxX9A061Oy3ysQT3LDo9uIpdJ8Y0kpakphoCvE+FBkenVaBZ8nji6g\n0OC28tEvVFBSNPdaMI/u8rDj3m2Eh/uQ5RRgIbupmaYv1GO0z04qTlxJsycwRat/gslEHJNGw7nO\nHCzjXlryyw869sYbb+T+++8nlUod5t1mn6qqBPyJ97V5HRkME4+/E1hmZhkpKLKyoCHrQDpTbr4Z\n3UlOVRUE4eQQwYUw63qCcb73xjBlGQbuuCCHhZnHn5M8NRimff0wg7u8yLJESUsWVcvzsGXPTp7z\nB5UIe/F0ricwuhsAU2bpgdceDE7xdCjArU4355pOzhRj4fSVGvJOt4x96UX0o9vQkUCWLMSLWog0\n1+O3SUxu3kWidTOyXktmywIqbv7Y9JTsjFMTZKcVle8818ZYOMFnFxZQtDvFa2tHiIZTVBabMVmm\nP3LcBRlc9LUiiubNrXojRVHY+1gnXWt3ocQnANBacqn68CKqripFlmcn9cmbiNHq97Ar4CWhKOQa\nTFyVU0yN1UF+bi4TExMHHb9582ZaWlpm5VoOJxJOMjwYft9uRDj4TlqWLUNHfpGN8y7KP7ATkVdo\nxWQWtxqCcDYRP/HCrIqnFb6zaRiDRuZ35xccV7G2qqqMdwRoXz/MeGcArVFD9YV5zFuWi2kOFfPF\nQxMMtT7IyK4nAHBXXYLFVQbA7niUf/OMscJk5SuO2UmlEE5/ibYhYqvWoG5cj2FqD3rSpDQOYlUr\nCTXPJyAn8Lyxk9Trr6MxGXCdW0/28mZcSxehNZ/6AHtNxzgv93ppdNuQHp7ixYEwjednc9G1hZTV\nnNwdlA8iMhll6507GN/ZjkaOoig6bMVVNH6+kawq56ycU1VVuiMBtvo89ESCyEjU2hw0ObLYsf4V\nrvn6lXR1dfGVr3yFn/3sZwDs27eP6urqWbmetyUSaUaHwgdavL5dHzF1UHG1hvxCK43nHFxcnTFL\nOzqCIJxepNO9Vd3xaGlpUbds2XKqL+OscFvrKA92+Y5ryraSVhna5aX95WF8QxGMGToql+VSfm42\nujnWZjA4upedj33twOMltzyNRj+dQx5Ip7lmqJu0qvLXwnKcmrl17cKpFd+0n9jqNUibN6APdSKh\nktTlkJh/PqHGKvz/P3vnHd5Wefb/zzmatiTLe8gr04kz7HiQkA2EAEkpZbbsUaB9oXTD2/Xron15\nS+nbRVtom7BHoVAoO00YGYRAvDKc4WxvS7ItWXud5/eHEjsmCYTEip34fK4rV9DROTq3HCE/3+ce\n36Cb7g1biAWCaM3JZM6tIHtBFekzpyXMJftE+dLzdbS5AsxdLVDCgtt+NJVJM9KHO6xj0lbTxean\n6gk4WpDlGAop5M8upeKmaegTNLI6GIuyua+HercTVySMWaNjhjWDGdYMXn7uea6//vr+cxMpJmIx\nBXunP+4XcVhZk73TP9BcrZXIzTcNuFYf/JOeaRyR/TEqKiMVSZJqhRCnNt04jKirHJWEsarNw7N7\nXNxUkvaZhEUsorB/o4Om1R34ekJYsoxUXTWWospMNKfYbfd48Dl3s/nFryFr9Iw/926SrPloDfH3\nK4Tg+452OqMRnrGNUYWFCoqiEHprE+Fn30TevBZ9sCU+MjZpDL6zr8VXPpZeRxc9G7eivNmMLi2F\nnPPPJnthFWkVpci64f0MtbgD3PnaVgLR2MH3A57eMLGYwGsQlO6EJIOB//rfMnKLRl75XywSY8tz\nO9n/9laI9iCEhC7FRunl5UxYXJSw+zpCAWpdTrZ5eokIhQKjiQUZeZSYU3noz39m3te/Puh8SZKw\n2WwnfV8h4s3Vh8qYDomIzjYf0ehAc3V2bjK2QjPVc3IPa65OQjMM0/ZUVFROb9SVjkpCaPdF+MnG\nDqamGfnW9OMbtxr2R9mzvovd6zoJ+aKkF5kou7gI29Q0pBE8PcTr2AWAreJLZE9aPOi5R9w9rPR7\n+GFGDhXGE29gVzm9UcJRAi98QORfK9Bufx9d1IEBiXDKZDyzb8U3tYCe/QforduO2LcTQ1Y6ts+f\nQ/bCalKnnxqX7OPlpe2dNHX7uHhSNtGIws6GXkx+hbQsA4aYzOemWrn0unFYRliJTF+7l7rlDfTs\n2I0sB1EUPanjp1Bx6wzSihPTUK4IwS6vm1q3g5aAD60kMcWSRmVqJjmGge+Drx8mLDQaDb29vVgs\nn70f5VBz9eFCoqPVSzAw0FydlmHEVmiitCyjPxuRm29CrzZXq6ioDBGquFAZciKK4J4P24kJeOBs\nG7pPEQZ+V4imNZ3s+9BOLKyQO9nKpHNsZI6znBap97CvG4DcqZ8fdLw26OeBni4uSLZwc8rILQ1R\nSQwxb5DAk+8Se20l2r0b0Cp9aNASypxOYNZV+Cbm0r1tJ64tm2HbJpLysyn64oVkLawmZfIYpAQ1\nDx8NIQR1HX34wp88eSgcEzze0Mq8ojR+PnsiD/6wAalF4as/LaO0cmR+xvevaaPx+QbCvW1IsoKk\nTaVwQRVl15WiMybmV6AvGukvffJEI1i1es7JtFGWkk6SRsudd97JQw89BMR/9hs3bmTevHkEg8Hj\nev2AP0pH62E9EQf/ePoGmqtNFh35hWbOXmA7rKzJRFKyOqVORUUlsajiQmXI+VOjg03dAR4420aR\n+di7l+5OP03vddBc3w0ICmdkULLQRqrt9Nrh723+EACNfiDu7liUb3a1kq/V8ats22khklROnqjT\nQ2D5CpT/vI2urQadCKDBQDi/Gv/ss/EUpdBd10hfzUdQA6axBYy98RKyFlZjHgaX7EO8vbebO1/f\nelznJutk7pxcyP99p5ZeR4iv/GTaiBMWkWCUTU9to2XtNiTFhVBk9On5TPvSDIrn5Sfsvh1BP3Uu\nB9u9LmJCMCbZwuKsAsabUpAliSuvvJIXX3zxiOuqq6uPKiwi4Rid7f6BCU0H/+5xDpxrMGiwFZop\nq8rq74nILzRjserV7x0VFZVhQRUXKkPK+50+lu/o4YqxVpYcZXa9EALnPg9N73XQsd2FRiczfk42\nExfkYUozDEPEJ8e+9x/C07GVpLRitPp4fXlMCO62t9GrxPinbSwWWS03OJOJ7LMTWPYm4r130Ds3\noSdKTDYTHjOH8PxZeDJ1ODdsxrt2NQCWU+iSfTwsr2vm/nV7GZuaxK8WT+bTlqO+A0H+8eNGNFqJ\nu/6nnAnTUj/lilNH7/4+6pbX4967B1kOIxQjaaXTqfxyOSm2xIznjSoKO70uat1OOoJ+9JJMeUoG\nlamZZOiN/ed9fKFvtVpxuVxAvLna0RUY7BfR4sXeMdBcrdHEm6vHT0plwfmDm6tV0zkVFZWRhCou\nVIYMZzDKDz5qZ0KKnu/PyBn0nFAEHdt62fFeBz0HvOiTtUy5IJ/xc3IwJGgqS6JprX0a+463AJhw\n3j39x//icrIu4OOXmXlMMRiPdbnKaUxo8wFCy16HDe+hd2/HgCCqzSA0ZQmhBdX0GaI436/Hv+I/\nIElYp09k4l3XkLWgiqTckTOK2B+J8af1+zEpEnP269jw8IFPPF9RBDvqe7AVm/jqT8pIzxkZn+9d\n/9nPjpc2E/V0IEkCWZ/B2EXTmPalEjS6xIh7TzRMg7ubBnc3/liUdJ2B87PymWZJx6CJ33P27NlY\nrdZ+J+1oNEp2dh6r3tpEe4uPR/+8Nd4X0eYjGlGAeHN1Zk4S+YUWqs7O6RcRObnJI3KghYqKisrH\nUUfRqgwJihB8ZU0LDd0B/rFoDBOs8SyEElVoru9m53vteOxBktMMlCzMZcxZWWhP4wbCkNdBzeNf\nBGDs/K9jK7scgPUBLzd3NPMFs5VfZ6nlUGcSwfcaCT31JnLtGvT+vQCEDfnEps8jNL8cV9iDc20d\nwa5uJI1MWkUpWQuryJpXiSFj+Hf3wzGFV3d2EYwq/cfWb3Oy0t7L4l16iqTja8AuGGfmyq9OwDDM\n46DDvgj1j22hbcN2ZDwoioak7CLKrptBfnXOp7/ACSCEoDXoo87lpMnrQgEmmFKotGYyJnmgR2zK\nlCls3769/7qn/tZ4sKTJRzAw0NeSmm44YsxrXr4JveH0/W5UUVE5EnUUrYrKCbBsRzcb7H5+VpXL\nBKuBSDDG3g12dq3tINgXwZqXzMxrx1NQloGsOf0X3PvfjzdjFs68uV9YdEUjfMfexnidgZ9n5qnC\n4jRHURSCr2wk/NwKtFvXogt3YARCpgn4F9xCeO5Ueru7cKytI/zMv5B0WjLOmsrYL19K1tyKYXPJ\nPhb/bOzg5+/tOuJ4jl/md7+ZddosaJ07e6l/rB5P8z5kOQJKMhnlM6i6tZzkjMQYCUYUhW2eXupc\nDuzhIEZZQ3VqFhWpmRijGjpafbzf0sb1N3+OAy2bB11bUnwBtRu6yC8yM2t+br+QyCswYzKfnllb\nFRUVlU9ixIsLSZIuAv4AaIBlQohfHeWcLwI/AwSwSQhx7SkNcpTzfqePPzc6uajQwhVjrQT7wrz3\n0Ha8ziBZ41Oo/uI4ckqsZ8xiO+Buw7n7XbSGFAqrbwQgKgTfsrcRUBSeyisg+RRO+lEZOpRghMCz\na4m8vALdrg/QxnowIhNOnYL//MsJnjWB3gMHcLxfT/SRLQddssvIWlBF5tnlaE3D75J9LJ7b2s60\nbDN/v6QMJSb4+31badvj4QcPVI54YaEoCjtf28euVzcTC3QBoEnKZvxF05hy+QTkBP3/5oqEqHd3\ns9ndTSAcxezWMK7PhOwUbGu1s6plL92Ogebqw4VFxfRzeOqpF8gvNJOSqjZXq6iojB5GtLiQJEkD\n/BlYDLQCGyVJekUIse2wcyYCPwDmCiF6JUk6PlMFlSFhlzvEdz9oY1yKgZ9V5RIJxFj79x0E3GHm\nf2UyOROtwx3ikBPobQGgePZt/QuG3/XYqQn6+b/sfCboT7/G9NFMzOUj8PjbxN5Yhe7AR2iFFxkd\n4ZwKwvMXEJhRSM+2JpzrNxGr/RCtOekwl+zpw+6S3bbPy3v/bmXfdvcxzwloBDsmB6no0vLQu/VE\nIgrdnUEa72u6AAAgAElEQVRu/G4pBcUjK8NyOAFXkLpHt9BVuwNZ8qEoWky28cy4qZKcaRkJuWcs\nptBwwMlHTZ0caPYQ6YqBU+B3hBEKbAVkjUSuzcRfn76R7t79pFqzaNq5n9o9Z2G1prBq1aqExKai\noqJyOjCixQUwE9gthNgLIEnSP4AvANsOO+d24M9CiF4AIYT9lEc5SnEGo9y5roUkrcxf5hVgVGDN\n8p14HEHmfnnSGSksACL+HgBSC88C4B2fh7+5u7naksYl5jPzPZ9pRDt6CSx7C2XV2+g769ERQpaS\niRSeRfi8hfhKMnDWbqXnw/dR1kbQpVrIOX8W2QurR4RLNkDLbg8vP7KHnQ296A0ykyvS0WgH7473\nSTHqdUF65HifxTSLibyDAxQWfr6AWYuGf1rV0ejc7GTTE/X4OvYjyzHATHZVNZW3lGEcInM+IQSu\nnlD/ZKaWZg/7Drjpbg8gDrVFSJCRZaSgKAXbnIG+iEmlufh8vv7XcrkdZOUks3HjR0MSm4qKisrp\nzPD/hvxk8oGWwx63ArM+dk4JgCRJ7xMvnfqZEOKtj7+QJElfAb4CUFRUlJBgRxOBqMJd61pxhWI8\ndm4x2ToN7z+yk95WL2dfP5GckjN3kR0+KC70yem0RsL8t6ONqXoj/y8jMU2kKkNDeGc7wWVvINa9\ni6FnK3piRGUr4ZJzEYsX4s1PwvFBA73/WYF4M4YhKw3b5xcedMkuGTEu2UII3n+rgxceaiLZouOS\nm8cxd4kNk2Vw/b4iBNe+UE9DZwCjVmay1cQPri5HO0JL9pSoQuOLTez5TyMi5AAktOZcJn1+OhOX\njjmp0ievJ3xwxKtvkF9EwD/QXK2xyOhzNOTOtjBlXDqVE7IpKLRgMA4uGft4edMdd9zBX/7ylxOO\nTUVFReVMY6SLi+NBC0wEzgEKgDWSJE0XQrgOP0kI8TfgbxCfFnWqgzyTUITghx910Ngb5A9z8ilN\n0bPhyd3Yd/dx1pfGkT99ZBlqDTURfw8avYmIRss32/ejAH/MKcAwQhdto5nQR7sJPvY60kdr0Hua\nMCCI6HIIlX8BZck8PCZwrKvF9a9/gSJIsmVR+MULyF5YTcrksafUJft4CAdjPPeXJj5c1cnkyjRu\nvmcK5mPs5L+8o4u6jj5+tXgyl5eOzAwFgM8RoHZ5A84tTchyAEXRYSmeRMUtFWRO/GxTtoLBKB2t\nviP8Ivpc4f5zkk1abIVmSmamE0iP0ZcWJSlHx9TsdCpTM7EZTYNe0+PxkJWVhcPhwGKx9B//8Y9/\nzL333ntyb15FRUXlDGSki4s2oPCwxwUHjx1OK/ChECIC7JMkqYm42Nh4akIcffx+i4OVbR7uKc/m\n3DwzG5/bQ3tjLzMuLaa4Omu4w0so/t5mOra8hN6czf3dXWwOBflzTgFFuuGtu1eJoygK4ZWbCT3z\nFnLDGvTBZoxA2FhMaPZ1KEtm4455cayppe/RZwAwjc1nzA2fJ3thNebxhSO28dbe5mf5fVtp3+9j\nybVjWHLNmCMmrwkhuPXfm/mozUUkJpiWbeayySMzo9b6USebn6kn6GhBlhXQpGCbXcaMG6eh/xTv\nm2hUoav9UBZiQEw47YH+c3R6mbwCE1PKMsgvOmg4Z0vigMZLg7sbdzSMWWvgAquN8pQMTNrB9/R4\nPFitVg6Na7darSiKwmgc366ioqLyWRjp4mIjMFGSpLHERcXVwMcnQb0MXAM8KklSJvEyqb2nNMpR\nxAt7XTyys4cvjU/lhgmpNLy8n+a6bqZeVMCEuSN3d3SoCPV1AtBYfi1P9vXyZWs6F5iOdCJXOXUo\n0RiBFz4g8uIKtNvWoYs6MCARtpQQmPcVlIvOwtVtx7GmBu+fHwXAMmkM42+/gqyF1ZiK8ob5HXw6\nm9Y7ePK325E1Enf8vIwp1QPNzO/u6+aFbR0ABCIx1jX3cnFJNjaLkS9MzhlRYikWibH52R0ceLcR\noj0IIaG32ii9opzxi44sV1UUQbc90F/GdOhPZ4cfJRZf5MuyRI4tmeJxKcxeaOsXEpnZSf3O1fZQ\ngFqXg7fcbUSFoDDJxDmZNiaarWg+9vNpb28nPz//iFiefPLJBPxEVFRUVM48RrS4EEJEJUm6C1hB\nvJ/iESFEoyRJ9wI1QohXDj53gSRJ24AYcI8Qonv4oj5zWd/l4xd1nczLNfGDGTk0rmhlzwd2Shbm\nMfk823CHd0qIhjx0mLL5XcpEKgxJ3J0+MneFz3Ri/hCBJ98l+upKdHs2oFXcaNAQyigjNv96YueX\n07NvP441Nfgf+GvcJXvahLhL9vxKkvJGboYtFIjSuLGH4MF+gNZ9Xta82kZxiYVbfzBtkCt2bbub\nb7zRSJJOJscUn1K2aGwG9y+ejG6E9IgAuNu81C1voHfnLmQ5hKLoSZ0whapbK7AWWQ42VwcPExG+\ng87VXsKhAdO/zOwkbIUmyqqy4uZzRWay80zodEe+15gQ7PC4qHU5aA360EoSUy3x0qdsw7FHBm/a\ntGnQ4zfeeIMlS5YM3Q9DRUVF5QxHdehWOS52u0Nc/84B8kw6njy3iNZ1XWx9o4Wxs7KovGLsiNod\nTSS7ap7iK9ocvKmFvFIwnjytaoJ1qoh1e/A/shJlxUp0rTVoRAAFA2FbJdJ5i4jMm0h3YxOONTUE\nO+Mu2akzJpO9sDrukp05/C7ZH8fjChM7uAPv7g6xYWUHG9/pIhiIDTpv3lIbV3x14hGL6PnL19Pl\nC/PMlTOoto2897dvTRvbnm8g3NuGJCsITRq5syeRPruAjs7AoGyE3zfQXJ2Sqo9PZio46F5dZCav\nwITR+On7Yb5ohE193dS7u/FGI1i1eipTM5mekk6S5sjra2pqOOus+OS3F154gSuuuILCwkJeeukl\nqqtHjaGuiopKAhltDt2quFD5VN5t9/DTmk5kCZ5dNAb/ph7q/7WfgvJ0Zl07AUkeHcIC4M7NK1hl\nymd53hjmJ49cf4AzhcgBB4FlbyLeewe9fRMyEWKSmfCYWchLFhGqKKC7ZiuONbWEe9xIOi3p1VPj\ngmJuBTrryPw3CodiPPOHndS81zXouE4vUzE/mzkX5pGRG89QaHUylqM0bTv9YeYsW88dZxXx7dnj\nTkncx0MkGKXhiUZa1m1DFm4UIeM1ZODKyKLNA+7eUP+5xiTtwTImE7aCeDlTfqEZc8pn72FqD/qo\ncznZ4XURE4IxyRaqrJmMM6UgH2Xz480332Tp0qWDjo0bN449e/Z89jetoqKi8gmMNnExosuiVIYX\nbyTGrxrsvLzfzSSrgV+fbSOy0039S/vJLU1l5jXjR5WweK6nnZXmQi5rXs388dOGO5wzlvCWZoLL\nX4cPVqN3bcOAQlSTTnjKRcifX0RgXCrODZtwrFxB9EUfslFPxqwyshdWkzl7ZLtkA/T1hPjbL7ay\nf2cfi64oJMuWDIBeLzN1ZsYRI2WPxQ6HF4CzC9ISFuvxEIsqdHX42V3bxYGV2zG4OtHLUYJCS7OS\nQatiAUlHnqyndLq5PyNhKzSTlmE4qaxnVFHY4XVR53LSEfKjl2VmpGRQkZpJht54zOtmzpzJxo2D\nZ360tbVhs42O8k4VFRWVRKKKC5Wj8pHdx482dtDlj3L75AzunJqJY4eLD/6xh8yxFmbfMBF5BNV0\nJ5oNe9dzr2KhtGc3NwU6hzucM47Q2u0En3gDuWY1ev9eDEBEbyNUfRWayxbhTdfiXFeH8/kXiPmD\ncZfsOTPIWlBNxsxpaIynhyt66x4Pf713C76+CLf/v2mUzzm+3g8hBP/a3sl+18A0pG12DwCTM09N\ndkZRBN2OwGC/iOY+Au29FEouciUvFgl6JBPhrLHkzy7mwmILtgIzWbnJ/c3VQ0FfJEyDu5tNfd34\nY1HSdQYWZ+UzNSUdg6w56jV/+tOf+O53v0soFOLtt98mJSUFWZZxuVyDRsyqqKioqJwcqrhQGUQg\nqvD7LQ6e3t1LsVnHk+cVU56RhH23mw1P7iI138TcW0rQHKWB8kzFo8T4b3+UZNnDXdtfYPylDwx3\nSKc9iqIQerWW8HNvotmyDl24PT4yNnkcwQW3oL3qPDwEcKyuofvhR1HCEXRWCznnzYy7ZFdOGREu\n2cdLv/Hdw7swp+j49gOVFE44vgVtOKbw99pm/rBhP1pZ4vAlemVeCmlJQ9v3I4SgzxU+YkJTR6uP\nUCjeCyKjMMYYZHzUhUkTIKbIyOlFVFxXSfGsxEyNE0LQGvRR63LQ5HUjgAmmFKpSsyhOMh8zA/KT\nn/yEX/ziF/2Pv/e973H//ferI2VVVFRUEoTac6HSz5aeAD/8qIN9njDXTkjlW9OzSdbKdDd7Wfu3\n7SSnGlh4RymGT5lBfyYhhOBb9jbe8vbyK3sNl82+ZbhDOm1RghECz60j+vJ/0O5cjzbWjUAmnDoF\nZi9E/uI5uHvsOFbX0FO7DRGNYchMJWtBFdkLq7FOL0HWHn1XeiQTCkR59sEmat7rYnJlGjfdM+Wo\nPRRHI6oo3PLSZj5sczE508S/rq4aUodtvy9ymNncQb+IVi8+T6T/HItVf7CMyUSaHvwbD6DYW5Dl\nKIpIJqt8ElVfLiM5IzHlaBFFodHTS53LgSMcxChrKLNmUGHNIFV37IzVnXfeyUMPPTTomF6vJxQK\nHeMKFRUVlcSg9lyojDoiiuCv25z8fUc3mUYtf19QyOycuEutu9PPumU7MJh0zL998qgSFgBP9/Xy\nhq+Pq5pepcxk+vQLVAYR6wsQePxtoq+vRL//Q7TCi4yOcHY5sXNuRb58Du69+7GvqcH1w1+DIjDm\nZVF45eK4S3bpuBHnkn289NiDfLiqk/Ur2nF1h7j4hrFc8KXi4yoPCscUvvb6Vj5qdRGIKnxtZjG3\nVhaesLAIh2N0tvloax6cjejtOby5WoOtwEzFWdn9Y17zCsyYLVp2vLKXXa9vwReIN6BrkrKZsGQ6\npZeNR07Qv48rEqLO5WRzXw8hJUa23shF2YVMsaShO457PvPMM/3/nZycjM/nS0icKioqKiqDUcXF\nKKc7GOWOtS1sc4W4pDiF78/IIUUf3x32OoOs/dsONDqZBV+dTNJx7raeKWwOBrivu5PqvmaW7F1F\n7tXLhjuk04Jop4vAsrdQ3n4bfXsdOkLIUhKRgmpiFy5GWlKBa8t27Gtq6fvmOwCYxtgYc/3FcZfs\nCUWn7WjjSERhywdOPljZwY66HoSAkvJUbry7lInTj6/xepvdww/f3sk2h5crp+RSnZ/K5aXHV2oU\niyrYu/yHiYh4NsLR5edQklqrk8nLN1EyJT0+pemgkEjLMA76uQdcQeqWNdBVvxNZ8qEoWkz5E5hx\nYwU50zKOEcHJIYRgv99DrdvJHl8fEjDJnEpVaib5RtMnfi7OP/983n777f7XcblclJSU0NTUlJBY\nVVRUVFSOjiouRjm/2+KgyR3i93PyOT9/oAbc7wqx5m/bUWKCc+4sxZR+7MkrZyKuWIxv2FvJ1ur4\nrx0vICMwZYyccZ8jjciuDgLL30SseRdD92b0xIjKKYQmnoP284tRFkyi56PN2NfU4n31FQAsJcVx\nl+wFVZiKT68pPUIIGjd24/NE+x+37vby0bud+D1R0rIMXHh1MWcvziMz99PLhYLRGKv2OonEBA9v\nPMA+V4CvzxrD12eNOer5iiLocQYPK2mK/93V7iMajasISYLsvGTyi8ycNTe3f8xrVm4Smk8YxtC5\nyUHDE/X4Ow8gyzGQzOScdRaVN0/HcAIjYo+HUCzGVk8PdS4nPZEQyRotc9JzmGHNwKL95HsebfJT\ne3s7NptNFRYqKioqw4AqLkYxm7sDvLzfzZcnpQ8SFiFvhLV/30HYH2XBV0tJyUkexihPPYoQ/Lej\nDXs0wrO2sXi796A1pgx3WCOO0MY9BB97HenD1eg9TRgQRLRZhMouQXv5BUTLbHSvr8e+ejX+p/8B\nEHfJ/trVZM2vIsk2cl2yPwkhBP98eBdrXm0bdFyrlSibncXsC/OYVJ6GrDm+7EtUUbh7xXb+s8cZ\nfx1Z4u+XTGfhmAyEEHjc4cN6IjzxbESrl1BwwGgvLcNIfpGZqeWZ/dmI3HwTev3x9agoUYXGF5vY\ns2IrIuwEJLTmXCZ9oYyJFxUnrPSpOxykzuVka18PYaFgMyZzcXoRk8ypx1UC9vFMRmFhIc3NzQmJ\nVUVFRUXl+FDFxShFEYL76rvINGr4aulAiUMkEGXtsh34ekLMv30y6YUj04QskSxzd/Ou38uPM3KZ\nppXZAKSPmTPcYQ07iqIQfnsLoaffRK5fiz54ID7hyVhE6Ozr0F9zIRGbCeeaWuwv/ZvgQ864S3b5\nJAqvOJ+s+ZUYMofXk+FkEULw0rI9rHm1jXMvLWDh5wv6nzOl6Egyfbav1HBU4Yrna9np9HHDNBvn\nplnp6wrS+Z6D37Xso63Zi/ew5mqTRUd+oZk559j6vSJshSaSkk+sF8rrCFC3rB7n1l3IcgBF0ZEy\nZhIVt1SQMSExjt+KEOzx9VHrcnAg4EUjSZSaU6lMzSLP+OkbGUVFRfj9fpxOJ2effTYbNmygtLSU\nbdu2JSReFRUVFZXPhiouRin/3u9ma2+Q+2bmYdLFdzej4RjrHm3C3RFgzs0lZI0bfbv1HwV8/LbH\nzhJTCjekpOFoWgWA3pSYGvORjhKNEXzpQyL/fAvNtvfRRbowIBG2lBCcezuGG5YQ0Uexr67B8dfH\nCXe7kLQa0qunMvamS8icW4E+9czwEBBC8Mpje3nnpRYWXpLP5bdPOKHekEg4Rkebjw+burm/qYU+\noVDSLdHy9wM8dbAvwmDQYCs0U16dha3QQv7BbITFqh+SfpSWDZ1sebaeoLMFWVZAYyV/TjkzbpqK\nbohH2x4iEIuyua+HepcTdzSMRatjQUYe5SkZJGs//VdRTk4Odrt90LEPPvggIbGqqKioqJw4qrgY\nhXgiMX63xUF5RhKfL4oLCCWq8METu+je72HWtRPIK03MruVIxhmN8m17G4U6Pf+TlUeg9wC7Vt0H\nQFbJ4mGO7tSh+EMEnl5D9JUVaHdvQKu4kNEQSp+GMv8a9DdeQNjTg311Lc77/kjE7UU26MmYNX3A\nJdt8ZpXS9fWEWPlCM+++3MrcJTau/OrET13kx2IKjs7AYL+IVi/2Dj+OVIn9BTJ9FpkZbg0XZqaS\nX3EoE2EmPdM4pKZzALFIjE1P7+DAe41IsR6EIqG32phy5QzGnVc4pPc6HHsoQK3LwTZPL1EhKEoy\nc26mjYlmK/JxCKX09HR6e3sHHbvwwgsTFa6KioqKykmiiotRyMPbnPSGYjw0PwdJkhCK4MNn99C1\n003VlWMpnDH6duljQvBdRxtuJcby3CIssobtG+LTodLHziU5vXiYI0wssW4P/kdXoaxYha5lI1rh\nR0ZPOK8SZdEi9NedQ6i5FfuaWpx330fMH0RjSiJrzgyyFlSRMWv6aeOSfQghBF0tfoKB2DHP6Wzx\nUfNuFzs39SIUmHNRHl/6WskgYSGEoLc7OHhCU6uXjjYf0YgCxJurs3KSsRWamTErm586OggqCt+c\nOYavnT0moe/T3ealblk9vU27keUQQjFgLZlC1S0VWIsSk1WKCUGT10Wdy0lr0IdOkplqSacqNZMs\nw/H5YXg8HiwWyyBhce211/L0008nJGYVFRUVlaFBFRejjD19IZ7e1csVY61MTTMiFEHtC/to29xD\n2cVFjJ2VPdwhDgt/6nWwPuDjvsw8Jhvik7G89p0YrfmULv3lMEeXGCLNTgLL30K8+w76rgb0hIlJ\nJiLFZ6MsPR/dlXMINDZhX11L91d/ihIKo7OayTl3JlkLq0mvLEXWn56+J4oieHn5Ht55qeVTz83M\nNXLhF4upPicHU6qOpm29g7wi2lu9gwRKWroBW6GZydPS+zMRefkm9IZ4+WFNu4vgC208uHQqF05I\nXFP7vtUtND6/iYirHUlWkHRpFC08i7JrS9EaEmNG6ItGaHB30+DuxhuLkKrTc26mjbKUdIya4/t1\nYzAYCIfDQFy4PfDAA+zdu5e//OUvCYlZRUVFRWVoUcXFKEIIwf0NdpK0Mt+YnoUQgk2vNbN/o4PS\n8/MpWZg33CEOC2v9Xv7scnK52cpVKfGGYyUWIexzkmIrH+bohpbw1haCy1+H9e+hd23DgEJUk05o\n8mJ0l16IZskMfDVbsK+upeeGfyOiMfQZqdiWzidrYTWpZaenS/bhxGIKz/xhJx+u6mTeUhvTZ2Ue\ncU44FKPbGcDvj+APRdh1oJfV/9OCxx3uP8dk1mErNDNrvq2/JyKvwIzJfKTgEkLw43d28p/dTsIx\nBQmYVTD0pYeRQISGJ7bR+v42JOFGUWSMGYVMu7qcojmJGfcrhKAj5KfW5WSHx4WCYGyyhYtSCxiX\nnHJcPSIej4e0tDRisSOzSHfffXciwlZRUVFRSRCquBhFvNvuZX2Xj+/PyCbdoGXbf1rZvbaTCXNz\nmHJB/nCHNyx0RCN8197GRJ2Bn2UOiCufcw8AyRljhyu0ISO0bgfBJ95AqlmDwbcbAxDR5xGsvBL9\nly5Enj0Bz/v1OFZvpPfxJ+Mu2bmZFF5x0CV7yunrkv1xwqEYj96/jS0bnHzu+jEsurIIe4d/oC+i\nOe4Z0eMM9l+jN8jkFZiZXpHZn4nILzSTknp8zdWd3hA/e7eJd/Z1c97YDHLNBiZlmkg1Dl3Wp2ev\nm7pH6unbtxdZDqMoSWROLaPq1hmYEzRKOqoo7PC6qHU56AwF0MsyFakZVFozSdd/Nl+clJTBwyMe\nfPBB7rrrrqEMV0VFRUXlFKGKi1FCKKbw6012JqTo+dL4NHat7WDbyjaKqzIpv6T4tHVEPhkiQvCt\nrlbCQvBgTgFJhy2gD02JSi2sHq7wThhFUQi9UUf4H28hb16LPtQWHxmbPI7g/JsxXL8ETUk23atr\ncKxYifu3DwGQXGxjzHUHXbInnr4u2UdDUQRNW3p54eFddB7wY5ts4sOaDl59bQ+KEh/RJGskcm0m\nxpVYmb+ooN8vIiMr6YSbq8Mxha+/vpVNXR4uGJ/J7y6agu4TDOw+23tS2L2imZ0vbyLq60SSBLIh\nk3GLpzHtqhJkbWIEYV8kHC996nMSiMXI0BtYnFXA1JQ0DPLxZbXa29vJz8/nxz/+Mffee2+890sI\nnnrqKa677rqExK2ioqKicmpQxcUo4bGmHlp9EZYtKKS1xsGmV5rJn5ZG1VXjkIZ4Ks3pwv/1dFEX\nCvD77HzG6QeakYUSo2PziwBYskuHK7zPhBKOEnj+faL/WoF253q0UWd8ZKy1lNA5X8Dw5aXIqXqc\nq2twPPk8nl0HALBMLGbcbZeTvaAa05jTyyX7aMSbq0MHTec87NnmpmWnB48zAjEJgUAkKQRiYWyF\nZipnZfdnI7LzktEO4YLcF47yxKY2NnV5+OOSKVw0cWj6mULeMPWPbqH9ox3IeFAUDck5Yyi7vgJb\nZWJ6poQQtAR81Lod7PK6AZhgSqEyNYviJPNxC9GdO3cyefLk/se/+MUvuPfee1EUJSFxq6ioqKic\nelRxMQro8EdYtr2bxfkWCrqCbHhhH9kTU5h53YTjdhE+01jl87Dc3cP1KWl8zmwd9FzI0wVA7rRL\n0JvShyO84yLmDRJ47G1ir69Et/9DtIoHGS3hrHJiC2/BeOtFSNEg9tU12H/zV/zNHQBYp05gwp1f\nIntBFUm207eB39sXHihnaj04panFS8AbRYpIyBEZSYl/vlMyDYyflkL1whwml2dgMCa2b+SXq3fx\nxKa4g/eicRlDIiwc23uof6web+s+ZDkKSjKZFRVUfrmc5PTPVoZ0vISVGNs8vdS5nDjCQYyyhplp\n2VRYM7Dqjn86mMfjOaL0CWDjxo1DGa6KioqKyghgWMSFJEkycI0QQp0peAr47WY7ioCbzAY+fGYP\nGUVm5txcgiZBZRMjneZImP92tDHdYOT7GTlHPO/csxoAc9akUx3apxK19xFY9hbKqrfRt9WgI4Qs\nJRHJryZ2wSKMN51PyOHAvqYW+w9+Q7DDAbJEWvlkCi5fRPaCqtPOJTsYjNJxUDi09QsJL32ugebq\nJKMWs1GPMawj4o0fGzclhZmL8ig7O5OUNH1CY1yzv5uHa5oRxHf56zr6uGhCFtX5Vr4w6cjP2PGi\nKAo7/r2XXa9vQQnGRa8mKYeJn5vO5C+MQ05QL0xvOES928nmvh5CSoxsQxJLsgsptaSh+wz3PDRO\n1mIZPPK2ra0Nm+30z5SpqKioqBxJQsWFJEkpwNeAfOAVYCVwF/BdYBOgiosEU+Pw82aLh5vyLOz7\nxz6sOUnMvXUSWv3pPfHnRAkpCt/oakUC/pBdgEE6cqFk37ECgNTimac4uqMT2dVJ4JE3EGvexeDc\ngp4oUTmF8ISFaC5ejOHaBQR3H6BzTS2OO39GyHnQJbtqKmNvuJjMeZWnhUt2NKrQ1e4b8Is4mI1w\n2gP95+j0MrYCM1PLM0m16vH2RGlt8tKy20svYfLHmjjvC4VULcwhPXvodvNb+wK83mRHiKM//8/G\nDgLRGBPSTYDEZaU5/GThREz6E/uK9fcEqXtkE/aGJmTJh6JoMRdMpOLmCrJKE5NNE0Kwz++hzuVk\nj78PGSgxp1KVmkm+0fSZenCefvpprr/+egAqKyupra3l2muv5eGHHz5CaKioqKionFkkOnPxJNAL\nfADcBvwQkIBLhRANCb73qCeqCO6r7yJHr6H4Px0kpeqZd/tk9Emjtxruvp4uGsNBHs4ppFB35G62\nEAqB3gNYC6owmI4cUXqqCNXtI/jo60gbVqPv24EBQUSbSXD6xeguvxDDJdUEtjTRvqYWx/XfJ+L2\nxF2yZ04ne2EVmXNmjFiXbEUROO2BeCbiMCHR1eFHiR3WXJ2XzJjxKcw5x9bfFyEJaFjnoH6dg427\n7AAUTjDz+ZvGUTEvi+z8E3/PXd4QPYHIEccFgrtXbGd3j/+Y18oS/Plz01g07uQ+M+0NDjY/UYe/\nqws/Go4AACAASURBVBlZjoFkIXfmWVTcPB2DJTHZl1AsxhZPD3UuJ72RECaNljnpOcywZmLRfraJ\nVr/5zW+45557Bh07cCDe36Oa36moqKiMDhK9yhwnhJgOIEnSMqADKBJCBD/5MpWh4IV9LprcIa5p\nCWI2aFjwlckYjzKDf7TwitfNM3293GbNYJHpGLunB7emrbayUxhZvPwl/G4joaffRK5bgz6wPz7h\nyVhIaOY16K9ZguGcyfg3NtK6ugbnVc8Q8wXQJBvJnDOD7AVVZMwqQ5M0clyyhRC4ekP9ZnNtzXER\n0dHqIxIeaODNzE7CVmimvDq73y8iO8+ETjeQVVJigndeauG1J/YSjQqKSyxc+uXxzJibRWbe8Tk+\nfxK17W5u/FcDEeUYqQngwaVTOXfM0d3rJYkTngKlRBW2/rOJvSu3QsSJEBI6cy6TLi1nwoVFCSt9\ncoaC1LmdNPb1EBYKNmMy89KLmWSxojlKRu/T+Liw0Gg09Pb2qpkKFRUVlVFGosVF/zagECImSVKr\nKixODXv7Qvxxs4PxvhjTPTHm3zmF5NSRs/A81ewOh/ixo51qYxLfST92c604VPdyCsawKtEYwX9v\nJPL8m2ga16GLdGEEQuaJBM+9DeNNS0maXoDzg00cWL2O7t891O+Snb2wmuyF1aRXTRkRLtk+b2Sg\nJ+KgX0R7qxe/L9p/TkqqHluhmQXnFww4VxeYMBo/+WuopyvIE/+3jd1b3ZTPyeTy2yeQkXPyguIQ\noWiMH769g2yTnu/Pn3DUf/ock4Hy3CMbkk8Gb5ef2uUNOBt3oZEDKIqOlLGTqbylgvTx1k9/gRNA\nEYLdPjd1LicHAl40kkSpOY2q1ExyjZ8963PnnXfy0EMPIYTg7rvv5p577kGv1xMKhRIQvYqKiorK\n6UCixUW5JEl9xEuhAJIOeyyEEEP721oFgB2uILevbkYJxrikNciC20uxZA3dYux0w68ofL2rFaMk\n87vsAnRHWT1Ggn20fPQYsegh7ZsYcaH4QwSeXUP03yvR7l6PNuZCRkM4bRqheVdjvHUpxrwUnO/X\ns++l1+j5f9sQkSj6jFTylswje2E1qeWThs0lOxSM0dHm7c9GtLf4aGvx4O49rLk6WYut0EzV7Bzy\nCy1xv4gCM+aU4y/rEULQ2eJn8wcOVv6zGQRc/53JzFqUO+T+Gw9+eIB9vQEe+UIZ84oTPx2s+YMO\ntj7bQLC7BVlWkDVW8ufOYMaNU9AlJUYoBmJRNru7qXM76YtGsGh1LMjIozwlg2TtZ/81cOWVV/Li\niy/2Px4/fjx79uwZEOcqKioqKqOWhIoLIcTo7BoeRrb0BPjqmhbkQIxbd/m49MZJpNpGZu39qUAI\nwU+dHeyJhHg0t4jcY9SQt9c/R8eWl9Alp6M3Z2POKhmyGGI9XgKPrSL21ip0zRvRCh8yesK5FSjn\nLSLp1ouQjBL2NbXs/utTuDbtRMSUuEv2ZYvIWliNder4U+qSHY0qdHX4DxMR8bKmbkegv6lZp5PJ\nKzBROj2j37XaVmgmNd1wwgLgQFMfNe91seXDbpwd8UbuSTPSuOYbk8jMHTqB3OMPU9Phxh2Msryu\nmSum5CZUWERDMTY/s50DqxuRYr0IRUKfms+UK8sZd25hwu7bFfRT63ay3dNLVAiKksycl5XPRJMV\n+QT+jW666SaeeOKJQcdSU1PZs2fPUIWsoqKionKak+hpUUbgv4AJwGbgESFE9JOvUjlR6px+7ljb\nijEY48btXi6+voSMMaO73vmfHhcve918Iy2LucnmY57XteMtAM66+Z9IJ1Bv/nGirT34l72BeOcd\n9F0N6AgjSyYiRWehXHQ+STefD8EA9jU1OO77C+6tuwFILsql+NqlZC2oxlKSeOd0RRF02wO0tQ7O\nRnS1+4gdaq6WJbLzkikal8LshXn9JU1ZOckn7Fx9NBred/DIrxqRZYlJM1JZdHkh02ZlkJY5tB4O\n7mCEq56vo6UvnqXKNRv4/rzxQ3qP/nu1eqldXoeraQ+yHEIoBlJLplJ5awXWgmN/Hk+GmBDs9Lqo\nczlpC/rQSTLTUtKptGaSZTgxgbZz504mTRo8mjk7O5uurq6hCFlFRUVF5Qwi0WVRjxPvu1gLLAWm\nAt9M8D1HJRu6fNz1fivWsML1jR4uuHoCOSWJqds+XdgWCvLz7k7mJpm4M/XYU3xcLbVE/D2kFc86\nKWER3t5GYPnrsO49DL2NGIgR1aQSnnQ+2ksvIOnq+QhHN/bVNTj++wE8TfEpOuaJRYy79XKyFlZh\nHpN/wvf/JIQQ9LnC/U3Vh/ojOlq9hEMDzdUZWUZshWamV2b2ZyNybIObqxPBpvVxYTGmxMJ//byM\n5AQOHvjxO010eEP86XNTKUxJojDFiNkwtF+Fe99pYdsLDUTc7UiyQNKlU3TOTMqvm4xGl5iErjca\nocHdzSa3E28sSqpOz3mZNqanpGPUnNj7Ky0tZceOHQD09fXx+OOPk5uby/333z+UoauoqKionEFI\niayRlSRpy2HTorTAR0KIyoTd8Diprq4WNTU1wx3GkLG6w8u317eRFRVc09DHuVeOpbgqa7jDGlY8\nSozLWvcRFAr/LhhHxicsrmqevJZQXwdlVz2MJfuzGeeFPmgi+PgbSB+9h8EXzz5EdLnEps1Dd9VF\nGC6pxr+/Le6SvboW/4F2AFKmjid7QTVZC6pIzh9al2yfN0JHq3eQkGhv8eHzDoxZtVj1/WVMtoMT\nmvLyzSQln/oxxZs/cLDsvkaKJlr42i/LhzSGcEzhl6t3sd8VL7GKKoKadjffnj2WO84qHrL7AEQC\nERoeb6Rl/XZk4UZRZIyZhUy/poLCs3OH9F6HEELQHvRT53ayw+NCQTAu2UJlahbjki0nnPkqLCyk\ntbV10LEHH3yQu+66ayjCVlFRURlVSJJUK4SoHu44ThWnclpUNNElHqORla193LOhnbFGLZfV9DBz\ncf6oFxZCCH7gaKc1GuYp25hPFBYhr4NQXweSrDuuPgtFUQi9tYnws28gb1qLPtQaHxmbNIbg3Bsx\n3LCUpPmT8e7YT9uaGhw3vEigPe6SnVo+iYJLzyNrQRXGrJN3yQ6HYnS0+QaNeW1v8eLqGZjUY0zS\nYCswUzkr+7AJTWZSrIl1rD4WLXs8NG3q7X8c9Mf4z/MHKBxv5mu/GDph4fCFeK3JzqbOPt7Y5aAi\nN6W/hOvKKbncVjl0fQ7du13UP1pP3/69yHIElCTSp5VReVsF5gQNUogqCtu9vdS5nHSGAuhlmcrU\nTCqsmaTrT24qnFarJRaL9T+eN28ea9euPdmQVVRUVFRGCYkWFzMOToeC+PgddVrUEPLaATc/2tjB\n9DQjVzX0YUjVU7Igb7jDGnYe7+thhc/D99Kzqf6E8ZpKNMzudx8AYPw53znmLq8SjhJ4YT3RF1eg\n2fE+uqgTAxLhlMkEz7kD481LMZUV4drcxIE1NTj+tJyQo/egS/YUiq+/mKx5FehTT+zjHosqdHUO\nNFcfGvfq7BportbqZPLyTUyako6tKJ6NyC80k5ZhTHjfxvHS1ern9/9dTygQG3R8XGkK//XzMpJM\nQ/d19IcN+3m+sQOAm2cU8MMFE4bstSEuMne9dYCd/95MzNcJCGRDJuMvnMbUK0qQtYkpI+uLhKl3\nO9nk7iagxMjQG7kgq4CpKWno5RMvt7JarXg8HhRF4dVXX2Xp0qVceOGFvPXWW0MYvYqKiorKaCDR\n4mKTEKIiwfcYlbyw18XPazs5KyuZ7+oNbO/sovrGiQlb1Jwu1Af93N/dxfnJFm61Ht3w7BBNb/8v\nruaNWHKmkFN60aDnYt4ggSffJfbaSrR7N6BV+tCgJZRZhrLgJpJuW4q5KIOe2m20r3oXx0/r4y7Z\neh0Zs8rI+moVmbPL0VlMxx27ogh6nMEjRERn20BztSRBdl4yBUUWZs7NI7/oUHN1EpoTNHE7FYRD\nMR75361odTLf+2M1KWkDmRNDkmZIBVAwGuPNXXYumZTNz88twaQfuq+5UF+Yuse20LFxOzJeFEVD\ncu5Yym6owDYjMRlDIQTNAS91Lie7fG4AJpqsVKZmUpRkPqmfXXJyMoFAoP+xx+NhyZIl6khZFRUV\nFZUTJtHiQv0NlQCe2tXDrxrszM818evKXN77zRYyxpixTTv5UpvTmd5YlG92tZKr1fGrLNunLroC\nvc3IWiMlF/wYgKi9j8DyFSgr30bfXoNOBNFIRsK2KpTF55N064XoUgx0f7SVlmdexvlBA1HvgEt2\n1oIqMo/DJVsIQZ87PGjMa3uLj/ZWL6HgwK5+ema8uXpqeWY8E1FkIdeWjE5/+k14fuGvu2jb5+OO\nn5eRnZ+Y0cjd/jB3vdFIizuAJxzj8il5QyYsurb10PBYHb62/chyFISJrIpKKm8tIyl1aKdZHSKs\nxGjs66XO7cQZDpIka5iVls0MayZW3cmVtVksFrxe76Bjd9xxh+qmraKioqJy0iRaXGRLkvSdYz0p\nhPhtgu9/xvFEUw+/3mRnUb6ZB2bZ2LWqnZA3wpxbSkZM+ctwoAjBPfZ2nLEYz+ePwar55AV42NeN\nv3sv2UWLCf/qLUKr30Xv3ISeKDHZQnjcfDSfW0zSTeehkwTODzax78En6P5wM0owjDbFRNaCarIX\nVpFWOQWN4eiLvYA/QluLb7BfRIsXn2egudps0ZFfZGbOOTZsBQNN1knJw++8PRRsfLeT9W91sPiq\nIqae9cnZpM/KR60u3thlB6DR7mGbw8vSkmxyTAbOLkg9qddWFIXtL+1h95tbUILxe2iSc5j4uelM\nvmQccoJ8R3rDIercTrb0dRNSFHIMSSzNKWSyOQ3dSdzT4/Gwbt06lixZgs/n6z/+y1/+kh/96EdD\nEbqKioqKikrCxYUGMJMou+NRRqc/wu+2ODjXZuY3Z+cT9URoWt1BQXk6GUWJmZl/uvBXl5PVAS8/\ny8xl2qfM8g817OfAK/dDBhgffwnDlgBRbSbhaUvRXnYhSV+ciy4QxPF+Pbt/8RA9NY1xl+x0K3kX\nHXLJLkE+zNk4HI7Reai5+rBsRG93sP8cg1GDrdDMjLOysRWYyT84pSkl9eQacEcynS0+nn2wifFT\nrVx849hBzzXaPdh94WNc+en4IlH+39s7ATBoNMgS/GjBBK4tO7lxvv6eIHXLN2Fv2Iks+1EULebC\niVTcXEHW5MQY7Qkh2Ov3UOdystffhwxMMqdSlZqFzZh8UhsHHo8Hq9XaX+okhMDtdvPcc89x2223\nDdE7UFFRUVFRiZNocdEhhLg3wfcYNTyysxshBN+fkY1Olti0ohWhCKYvTZzD7+nAhwEfv+91cLEp\nhWstRy8NC77XSOjJN5Dr1qD376Pn9gxAxmRaivLbS0haUkGkpw/H2lp2fu+3uBp2HHTJzqDgskVk\nH3TJVgQ4OgPU13QPykbYO/0DzdVaidx8ExNLU+NZiIPZiPRM45Cazo10euxBHvrpZvR6mVu+N7W/\nJ0QIwWtNdr67YvtJ3yMzWcdLV1eTYz55gdZWa2fz0/UEupqR5RjIFnJnzqTilmkYzImZrhWKxdjc\n102920lvJIxJo2Vuei4zrBmYj+Emf7y0t7eTn390oWWxWFRhoaKioqKSEBItLkbPSirBdAUi/HOv\nmy+MsZJv0uNq97G/xsHE+bmY0hNT83064IhG+Za9lTE6Pb/Iyuvf4VUUheDLHxF+fgXaxnXowh0Y\ngZBpAr7zbiSW9CaWnCkk/eouHGtqsX/9f+Mu2UKQXJRL0TVLMZRNo1eXzt5WH+tWe2l/6kM623xE\nowPN1Vk5ydgKzVTPye0vZ8rOSUYzyhvre51B/viDBvyeKHf9TzmpmfHFvzcU5XNPb6TDG6LKZuX7\n88ZzMtV8xdYkrMYTX4QrUYUtz+1k39tbIdKNEBI6Sx6TLytj/OKihJU+OUIB6txOGvt6iQiFfKOJ\neRl5TDJb0QyBQzxwhLB47733WLhw4ZC8toqKioqKyrFItLhYlODXHzUs39GDEILbSzMQQrD5tWZ0\nRg2lixLj6Hw6EBWCb9tb8SoKj+cVkxxW8D27msjLK9DtWo821osRDeG0KQQvuBLDTRfQaf8Hvv3N\nROtT6FsH6/94DwC6AhvMOxe7dQzNfQY6PvARfKcFaAEgLd2ArdBM6fSM/glNuTYTesPp11ydaFzd\nIf74/Qa8rjB3/U85xSUDI3j/sbWdDm+Ir80s5obyfNKThsdvw9vlp3ZZA93bmpDlIIqixzp2MhVf\nriB93P9n777D46rOxI9/752m0cxoRhr1bsuWbblIlg3YYFxoBuNAKAktwfSw2ZQlm4RkU35JSFk2\nhSSbsks3LYSFQAimBXDBFINVLNuyJMuSrS6NZjS9z72/PwQGgbEtacaW8fk8D8+DrmbuOVPk57z3\nnvd9U9PZXlFV2gMe6t3DHAj50UgSVZZMaq3Z5B+mZPLR2rx5MytXrqSoqIienh7sdjtOp5N3332X\nxYtPmt5NgiAIwnGW0uBCVVVXKs9/shgKxXiyw81F5VaKTXoGWtwM7fVSfVEp+uPQUXmq+P2Ig23h\nID+uHyDv1j8RPvAOWjWAjJ5oXg2Js84i/aY1GIoy8bd30f7cP+n7ZwsavwRk4skw0Z0zhw6pkIBi\ngX1gMisUlmg47czC0ZyIUguFxSbSTZ+O5OpUUhSVrjYvD/+mBe9IlH+9o5ry2R8s1CPxBA809HB6\nSSZfXzLtMGdKna43+9j1eCNhZw+yrCBpbBQtW0jNtXPRpaXmbymYiNPkGd365I3HyNDqWGEvYIHV\nTvphGjwerRdeeIE1a9Yc/Lm3txeA4eHhSZ9bEARBEMbr5F2ZnkDua3GRUFVunm1HSYzetTDZDVQs\nzTveUzsu4r0uXnnhDf58VgWXvLiJy373JxJSOrGSU1HOPxvtF87GE1DpeasF951PIO3dgz7oQUXC\nqc+jN6MUh7WMzPLRrtUXlJgP3o3IsOpP6qpb45WIK7Q0jND09jA7tw3jdUUxGDX8y48XML3KSp8v\nTPy9Hh2/37YfRzDKr1fPOaZzjEcS7Hi0ma4tzUiJEVRFxpBZRNXnFjBtRerylQbCQercw+zxj5BQ\nVcqMZs7OKWKGyYqcpO/YR7+rkiTh8XiScm5BEARBmAgRXExxjlCcJzvcfKbMSolZT+e2IbyDIZZ8\nccZJ1TAv2tpH+N7n4fWNOLX9fP8Pv6Cyo5t1rw5Tf+1dDE8vo78/QKB5H+nX/5HCYBfpSggdEh5r\nEd7aU1ELVaTwFq5Z9w3ySvJOquTqVAj6Yvz5R0107vFiMGqoWpTF/CXZzDvFTrpFx6/f7OB/t3eN\nec5tS6expOTY9GPxdPmou68Bd3s7shxFVQzYZs2j9sYarEWpqa6WUBVa/aNbn3rDAXSSzIKMLGqt\nOWQbkpMbde+993LFFVeM6UkhyzJut1v0qRAEQRCOOxFcTHH3tzqJqyq3zLETjyTY/VIP9jIzRfNT\nUxJzKolsayf84HPwzhaCEQf9xnwOZMzirh98naAhndx/hPiD9lRy3+2j+PW3KYn2oo+HUTVadLMr\nyV2+mGlrTiXNNrqQ3PX3b+Lp2UtecZYILCbJ64rwxx/sYLA7yBdum82ilXnodB8Eu68fcHH39i4u\nmJHDqmmjvS3s6XqWlaY+sNj3ahd7ntpBzNOHJKvI+izKVs1lwVWz0ehSkyPjj8do9AzT6HESSMTJ\n1Ok5O7uI+RlZGI7Qc+Vo3X777fzXf/0XALfeeivxeJznn3+eZcuWiaBCEARBmDJEcDGFOUJxntg3\netei1Kxn98s9hH0xlq6b+ancuqMoCs5nG+h+8k0Ge/pxaA30pxXQP/0GwprRakNta6z0l5q55Plu\nVvp2Yhhph3AYjdFA9orRLtn20xagTf/4VeL33zNZc3ySiD8tXINh/vt7jXicEW798QJmL/wg0G13\nBXhiVz+P7+qjMtvEz86ZhTlJXbIPJxaK0fDgLnre2oOselEUmbScUhZcvZDiU/NTMqaqqvSGg9S7\nHbT63SjA9PQMFtmymZZuSdrf6C233MI999wz5pjZPBowX3DBBUkZQxAEQRCSRQQXU9gDH7prEfJE\nadv0XsO8shP/KmU4FKe/J0DPfi9dG/fSv2+YwbgOn84MzIb82RilBIWFJk6bm01BjpYWeYhX55g5\n67UGVj/xT7QWEzmrFpO7YjGZiz65S/b7Qu4esspPPzYv8FNqoDvAH763g0g4waXfn4PTDm90jdZt\niCsqP3itDWcwytxcC39eOy/lgYWz3U39/Q34DnQgyzFQjGTNr6b2xhrMOYdvpjhRMUVhj2+Ees8w\ng5EQBlmm1pZDrTWbTH1yGyL29fWNCSxMJhN+vz+pYwiCIAhCMongYooaDsd5osPN2tIMSs16tv9f\nB4qiMu+CE6thXiymMNj3kc7VXT6cw5GDj9EpUfJDfirVCAV5dko/s5DS00pIl2MMv9mIY8uL7Hm5\nlz9+/xqmHxjkK94ERb/5FraaWWO6ZB9JNOAkPas8Ba/y5OAdifKH7+0gEVe44DuVfOnNPcQUdcxj\n0rQy//f5WqpyUxcAK4rC3uf30/psE4ngIKAip+VQcd5c5l5WmbJcJE8sSoNnmCaPk5CSIFufxurc\nYqosmejl5G23WrVqFZs2bTpYQlaSJGw2Gy6XKL4nCIIgTH0iuJiiHmh1Ek2o3DInG3dfkP3vjjbM\nM9unZsM8RVFxDAbp6x4bSAz1B1HeW4DKqOTEvZT4DnBaqIf88DDZGdnYVy7BdNOlaPNtRJxuHK/X\n0/nTZxhp2IOaUJCLcnng9isxGA3cf/rpFK0a/9XhfZvuQlVi6E3ZyX7pJ4VYTOHen+4k4Itx088X\ncNu7bWQZdfx6dRWaD+WvFGWkkZ+EbtmHEvFGqbu/iYHtLciSH0XRYCqYTvUXa8ivzknJmKqq0hXy\nU+cepj0wWoVppsnKIls2JUZzUrcnLlq0iPr6+oM/n3HGGUQiERRFSdoYgiAIgpBqIriYgobDcf66\nz82FZRmUWfS8/njHlGmYp6oqbldk7J2Ibj/9PQFisQ8WQdm5RgqydVQFfeS1N1IytI28SD+SZCRa\ndhraz52Lcd3ZaDKMhAaG6duyjaEtdXh27h3tkl0y2iU7d/ki7rQb6PC5uTu/hCLDxBaunt4GAApr\nPpeU9+Fkoqoqf/1DKx17vFz3nSr+s/kAvd4w6y+tZnGhLeXjD+5y0ri+nkDfAWQ5DpjIqV1E7fXz\nMdpSE2xHlQS7vSPUeYZxRsMYNRpOy8xloTWbDF1yc3Z++MMfcscdd4w5Nn36dPbt25fUcQRBEATh\nWBDBxRT0YKuLaELlS3OyGWh1M9jmYcFnjn3DPL8v+l7wEDgYRPT1+AkG4gcfY83UU1hsZvm5xRSW\nmMmJ+MjcsAnD2xvRe/YgoRDXZBGfdQZc+h3SPncG6Wk6gt0DdD/7KkObt+Nr3Q+AuaKEadd/ltzl\nizBNK0KSJJ7xuXnC0cetNjur0ie21SYeDRByd1NQfTnpmaXJeGtOKpv+3sPb/xzg/KvK2GGOsq3R\nzX+eMyulgYWiKDT/rZ19L+5ECTsA0KbnUfmZBVSunYYsp2brkysaod7jYKfXRVRRyDcYWZNXyhyz\nDW2Sx7z99tu58847ufXWWw8GF9XV1TQ2NiZ1HEEQBEE4lkRwMcU4w3Ee3zfCmtIMykw6XnmuC1OW\ngYrTU9cwLxJO0NfzwV2I94MIz0j04GOM6VqKSswsXppPYYmZwhIThcVmzBl6IluaCT/8D+TtW9AH\nOwCIGoqILP4c+isvwHhhLZIk4d/Xzf7HnmNo83YCnaNdhDPmTGfGrZ8nZ/ki0ovHvsa90TA/HO7n\n1LR0vp6ZO+HX52h7dfQ1ZBRO+Bwnqz31Lv52bzsLlmaz6vOlnPnAW6wqt3PJnNRUYAo6Q9TdtwPH\njjZkOYii6LCUVlJzXQ05s1JTfllVVTqCPurcDjqDPmQkZlts1FqzKUxLT3pltuzsbJxOJwAGg4Gf\n/OQntLS0MGvWrKSOIwiCIAjHgwguppgH2967a1FlZ/92B56BEEu+MANNEpJU43GFwf4gfV0+ej90\nN2J4KHTwMTqdTEGxiTnz7RSWmCkqGe1cbcsyHFxkKYpC5B91RH/6Av6dW9FF+0gDoqYKwsuvJ+3a\nNVjOnIOqqnj3dND7v08ytGU7od4hkCRsCyqp/No15JxZS1qe/ZBzDSgKXx3swSTL3JVbhHYSC7zQ\nyAEAcmadO+FznGxi0QSv/q2bl/96gIJSE+u+OYcNHcP4owluWlSS9AV37/ZBmh5tJDTUhSwnQLZQ\nsORUatbNw2BOTengcCLOTq+Les8w7lgUs0bLsqx8qq12zFpd0sezWCwfq/Tk8/kARGAhCIIgfGqI\n4GIKcYbjPN4+wgWlGZQYtLz4Ug9ZpWaKFozviq2iqDiHQvT2+Onr+uBuxEB/ECXxXnK1LJFbkE7p\n9AyWrig8eDciJy/9kA3mlHCM4OOvE3vmZXRtb6JNuDAgE7VVETnnUtJuWINlfilqQsG9s439v3sU\nx5Y6Ig4XkkZD5qI5lF21huxlCzFkWQ87f1VV+cFwH52xKOsLysidxEIvFvbQ3/Q3ALSG1HRl/jRR\nVZWmt4b5273tOAfC1JyRw3nXT+OGDTtpcwaYZjOyuPDwn9/RSsQS7PprG52v7oK4E1WV0GUUMOfS\nGmacm7rta45IiHr3MLt9I8RUhaI0E8vtBVSabWhS1D+moKBgTGBx9dVX8+ijj6ZkLEEQBEE4nkRw\nMYWsb3MRTqh8aY6dts39hL0xlnzxkxvmqaqKZyRCX3eA3m7fwdyI/l4/0cgHydX2nDQKS8zMX5Rz\n8E5EXqFpTEflQ0l4Q4QeeIX4C6+g378NrepHRkc0t4bEqpsx3ng+GWU5KLE4Iw17GPrlAzheryfm\n9iHrtWSdOp+Kmy8j+4wadBbTUb8Pj/vc/MPv5bbMHJYYj/55h+LtawIgf+5FkzrPp000kmDzcZ1h\nkgAAIABJREFUsz0EfPExx7v2emnb4aagzMRXf17DrJpM/vjOft7t83DO9Gw+P69g0nctfAMB6u5t\nxLlnLxo5jKLosU6fQ+2NC8ksz5jUuT+JoqrsDXiodw/TFfKjlSTmWDJZZM0mLy09JWPqdDri8Tiq\nqtLf348kSfzbv/0bd911V0rGEwRBEISpQAQXU4QrEucv7aO5FgVIvLipn6IFWWSXjyYxK4pK514P\nPQd89PX46X3vjsSHk6szrHoKS8wsO6uYwhITRSUWCopNpBmP/mOO948Quu8llFdeRd9fj44IsmQk\nVnwKidXnkH7DuRjtFhKRKK53djH0yNMMv9FI3B9EY0zDvnQBucsXY19y6C7ZR7I7EuKO4QGWG03c\napt42dhELIR/qJWRA9sAKD/j1gmf69NGVVUe/W0LdZuH0OnHBphpJi2X3zqTMy8sRKORCcUSPNTY\ny6pyO39aO29S4x7Y2suuvzYSdfUiyQqy1kbxmbVUf6EKXVpq/ikKxuPs8Dpp8Azji8fI0OpYaS9g\ngdWOUZP8MX0+HzabbUz52KeeeorLLrsMVVUP80xBEARB+HQQwcUUsb519K7FLXPsNL/cg5JQmf+h\nhnltzSP89qd1AKQZtRSWmFi0JO+97Uyj/1kyJrY3Pba3n9A9G1C3bsLg3ImeBHHZSmTmSrQXrcb4\nhRWkpxuIB0MMv9XE0JbtON9uIhGKjHbJXraQnBWLyVo894hdsg/Hm0jwlcEe7BoNv8wtQp7EFfID\nb99Hf9NTABgzS9HoUtOt+UT08hMHqNs8xEXXTee8z5cd9rH/vW0/I+EYNy+eWPPGeCTBjkea6dqy\nG0lxoyoy+swi5n6+hvLlqSut3B8OUu92sMfvJqGqlBnNnJtTTIUpY1Lfq8Ox2Wx4PJ4xx+655x4u\nu+yylIwnCIIgCFORCC6mgJFInMfaRzi/xEJ2MEHDOw5mLMvHnP3Blf/XXhhNSv7yt2qYX5s96a0p\nkXf3EX5wA9K2zeh9bRhQielyiSy4GN3lqzFetgSTVkPM62dw8zs4Ntfh2r4LJRpHl5lB/rlLyVmx\nmMyFs8fVJfuTqKrKdxx9DMRjPFZYTtYkrirHQp6DgcXci3+D0Xr8+4NMFU1vOfjH+k4Wr8zj3M99\ncl7D47v6+NUbHXgjca6aXzjusrMjB7w03NeIe187shxFVdLInD2P2htryChMTe5LQlVo9Xmo8zjo\nCwfRSTILMuzU2rLJ1qemH0ZfXx/f/e53Wb9+Pbm5uQeDiyeffFIEFYIgCMJJSQQXU8D6thHCCZVb\nq7JpeqITXZqGqnM+WBAP9gVoqhsGYG6NfUKBhaIoRF/dSeSRF5Abt6APd41WeEorJbLkGgxXn0/6\nuQuQZZmI003fhs04Ntcx0tCCmkhgyM2i6KJV5KxYjG3eTCRNcmv+P+Bx8c+gj//IymPhJPfARwOj\n71VO5TnYihcmY3qfCn37/az/1R7KKi1c/fVZSJLEUCDCb9/qxB9NHHxcTFF4rcPJ4kIrK8rtrKs5\n+uBs3ytdND+1g7ivD0lSkfVZlJ89j/lXzEKj06TiZeGLx2j0DLPD4ySQiJOpM3B2ThHzLVkYNKkZ\ns7W1ldmzZx/8ef369bS1tdHX10dhoSh5LAiCIJy8pnxwIUnS+cDvAA1wr6qq//kJj7sMeBI4RVXV\n7cdwipPijiR4rH2E1SUWTP0hBls9LFg7tmHem5v7AFh1fgmacSzqlXiC8FNvE3vyJTR7tqKLDWFA\nImqpJLzsFtLWrcFy6gwAwoNOep7855gu2cbiPEqvPJ/c5YuwzJ6W9PKj76sPB/mla5Dz0i1cZ51c\nL4Owp4/h9k0A5M5enYTZfTp4XBH+9yc7iWbK5F+Vxz/2DaGqcE9dF32+CCXWsVf2L6zM5efnzCJN\ne+TFeTQQo2H9Lnrf2oOMF0WRMeaUMf+aGopPSU0/DFVV6Q0HqHMP0+Z3owAVpgxqrdlMS7ek7Lu6\nefNmVq5c+Ym/F4GFIAiCcLKb0sGFJEka4I/AuUAP8K4kSc+qqtr8kcdZgK8D2479LCdnfZuLUFzh\nS7Pt7Lx/72jDvDPGNpMbcYYBuPTqmUc8XyIYIfTIZhL/eBlt+9toFTcyGiL2+SjLribtxjVkzBpd\nAAW7B9j/yHMMbanD19IJgHl6MdOuu3i0S/b04pQt0t7nTMT52mAPhVod/5lbOKHxErEwiVgQgI7X\nf8/IgW1IsgZjVnmSZ3viiUUTbPp7Dy89foB4QmH3WgMvbdt38PdmvYYHL6lm0QTKyw7vddNwfz2+\nrk5kOYaqGLEvqKH2hmpMOanJcYkpCnt8I9R5hhmKhDDIGhbZclhozSZTb0jJmB/20cCit7dXBBSC\nIAiC8CFTOrgATgXaVVXtAJAk6XHgYqD5I4+7A7gT+Naxnd7kvH/X4rxiC/p2H57+IKcdomGeVitj\ntujQ6Q99FTnh9BF84BWUF/+JrudddGoIDQaiBbUoZ5+F8abzSSvIRFVV/B099Nz/NEOb6wh09gCQ\nMXsaFV/6HLnLF5FekporzYeiqCrfHOplREnwRGE5Fnn8W1iURJztD11BPOw9eMxgzqP6irvRpaWm\nrOmJQFVVGt9w8Mz9+3AOhJl3mp38C7N5/s02frxqJsvLRpsX2tK0mPRH/8+Aoii0bdhP2z+aSAQH\nANCk5VBx/nyqLpmBnIRmj4fiiUVo8DjZ4XESVhLk6NNYnVtClcWGfgLfm6N17733cvPNNyPLMolE\ngmuvvZaHH34Yj8eDxWJJ2biCIAiCcKKa6sFFEdD9oZ97gNM+/ABJkmqBElVVN0iSdEIFFw/tdRGI\nK9wyK4vmu1uxFaVT/JGGeYqi8uamPrKyx25biXUNE7r3BdSNr6EfakRPjIRkIla2FGXNuRjXnYXR\nZnqvS3Ynjr//k6HNdYR6B0e7ZM+fycyvXk3u8kWf2CU71f7kHmZrKMAd2QXMNYzvSrej7VV6G/8K\nikI87CVr+pnYShYBYMmbe1IHFr2dfp74Uxv7dnsommbipjvm8ZDDwfqGTnJNei6rKkA/zpyZsCdK\n/f07GKhrRZb8KIoWU2EF1dcuJH/+xEsGH46qqhwI+al3O2gPjAaPlWYrtdYcSoymlN5V+9WvfsW3\nvvXBPyfvl5Zdv34969evT9m4giAIgnCim+rBxWFJkiQDvwGuO4rH3gLcAlBamrruv0fLE03w6N4P\n7loEnBFOv77yYwumwf4AAOkmLdFd3YTv2wBvbkLvbsaAQlyTRWTOanSfPQ/jlWeSnqZ7r0v2Xhxb\ntjO0pY7I0HtdsmvnUHrl+eScWXvELtmp9mbIz+9HHFxstnKFZXyViAA63/gTsaCLzPKlGDIKKD31\nekz2aSmY6YkjFIjz8/W72d46gk4vMfNqO8bpZn6xr5umQS/LyrK4bE7+uAKLgZ3D7HiogUDfAWQ5\nDpjIrV1E7Q3VpFknXnb4cCJKgt1eF3XuYVyxCOkaLUsy86ix2snQpWbMD/vo36BWqyUWi6V8XEEQ\nBEH4NJjqwUUv8OEC+8XvHXufBZgHbHpvQZAPPCtJ0kUfTepWVfVu4G6AxYsXH/duVi90ewnEFW6Y\nmcmeu9vILDZRMOfji+y3H98NwDlvPo76zKsYgJi+kHDt5eivWI3xolMwyTJKPM5IfQtDm7fj2FpP\nbMQ72iX7lPlU3HjpaJfsjNSUAB2vwXiMbwz1UqEz8JPsiXV8jgVdaHTpVF348xTM8MQSjMZ55uVu\nHtvaTVuhAhUgodLqcoHLRbpOwy/Pm8PaWXlHPhmjV+mbn2pn34s7USIOALSmfCo/s4DKC8uR5dRs\nfXJFw9R7htnpdRFVFPINRi7MK2W22YY2RWO+b926dZx33nlcc801B48ZDAbC4XBKxxUEQRCET5up\nHly8C8yUJGkao0HFlcDV7/9SVVUPcHBPhiRJm4BvngjVojb2+ikx6TC2+wiORFh4aTmSJKEoCpEN\n9UQffwF551Zi9lp0OcuY6+8kvPw60r64BvPyKgASkSjON3cwtHk7w280vNcl24B9STW5yxdhX7oA\nbfrUah4XV1VuG+olqCg8XFBM+gQWje93Oi6oPrn7CKiqSvN2F999YQ8ttjgUwgXFdu66ZN6EGsUF\nHCHq79/BUFMrGjmEouiwlFay8IZasmeO/+7S0VBUlY6Al3rPMJ1BHzIScyw2am3ZFKaZUjLmh61d\nu5YNGzYA8NBDD3HNNdfg9XpFPoUgCIIgTNCUDi5UVY1LkvQV4CVGS9Her6rqbkmSfgJsV1X12eM7\nw4nxxxJsGwpwVUUmrc/3kVViwvJOI57/eBltyxtoE04MyEStc9iUtwqNVsbSMPpS48EQg6+9w9Dm\n7Tjf3jHaJducTvYZC8ldsZisUybXJTvV7nIN8W44yK9yipg50eo+6uj+d1me0l/flGrf5eYf6zto\n3+2h6yyJKlM631tdyaIi67gDi97tgzQ90kDI0Y0sJ5DkDAqWLmDhunnoTbqUzD+ciNPkddHgGcYd\ni2LW6DjTnk91hh2TNjVjftiqVavYtGnTmGM5OTkAIrAQBEEQhEmY8qszVVWfB57/yLEffsJjVx6L\nOU3W1oEAcRUqNrURdOuZ++Jv0Q3VoUFLNKeaxIobMN50AWlFdpRrXyPHAv0vbGVoSx2ud3ce7JKd\nd85Sct/vkq2b8h8lrwV83O1xcqUlk4stE8/5cO7bAoB0kgUXrsEwLQ0u6l8foqVhhIwsPctuKuOf\nPV1cfVoxpxQf/d2FRCzBzr+2sv/VXRB3oaoSuoxC5lxazYxzU5eT5IiEqHMP0+wbIaYqFKeZWG4v\noNJsQ5Pisscf9uHAoqioiJ6enmM2tiAIgiB8mp1cq7PjLD7gJnTfS7yMHmtRMbp9fsyhEayGdKI3\n/gjjDasxZo9eNY24POxZ/yJnOreSNzBI824FQ04WhZ9ZRe6KxdjmJ79Ldir1xKJ829FLlT6N79uP\nbu//J9n/1t0AWPKrkjG1KSvoj7G3yU1Lg4uWhhEcfSEAbHYDn72hguVri3i4uRd6YHnZ0TUf9Pb5\nqb+vEVdLO7IcRlH02CqqWHhjDZllqamwpagqbX4P9R4H3aEAWkmiypJJrS2bPMPkurEfrcrKSvbu\n3cvq1at58cUXsVqt5Obm0tbWdkzGFwRBEISThQguUiy2b5DQvRtQt2zEMLwTSVZ567v3s9DhJWbM\nYu6tp2CbN5pGEh500vd/L+PYvB33e12yTRoLGeeuZNYly8iYk7ou2akUURW+PtSDAvw+rxjDJJJz\nVVUh4hsgPasca1FN8iY5BSTiCp17vAeDiQN7vagK6NM0zJxvY8maAqYtsJFXnI4kwQF/mPvqu5mb\nYybfknbYc+/f0svuJxqJjvQiyQqS1kbJ8kUs+EIVOkNq+kQE4jF2eJ00epz44jGsWj0rswtZkJGF\nUXNs/ukpKiqir6/v4M8vvfQSAG63+5iMLwiCIAgnGxFcpFC0aT+Jz1+OQQ0T0+YQnr+WHZetxac1\nUSpryZ6uw2KNsf/RDTg2b8f7Xpds07Ripq27iHccmWyqj/Ldq5ZgnX7i9m240zlEUyTMH/OKKZtk\nKdG9r/wCgJxZq5MxtSmj/0CAu+/YiaMvhCRDWWUGq68oY/bCLMpnZfBs+xDfeLWVWM/YQme2NC2/\nWj3nkOeMhePseLiZ7q3NSIobVZHRZxUx74oaypYVpe61hIPUux3s8btJqCrl6RbOzSmmwpQxoUTz\niWhtbWX27Nljji1btozXX3/9mIwvCIIgCCcrEVykUOSRF9CrYWLf+j3pN6xElmXebBhA1z5CZd0b\n6JQe3v7L6FVVy+xpVNxyObkrFh/skr3+G2+AFKOk/MRNMH3B7+Vhr4sbrFmcZ5p4gBRyd9O+8dcE\nHHsByJ+7NllTPO4a33Dw0K/3kGbUcP3tVcxZlEW6+YOk5id29fGD19o4tcjG2dPHNjxcOc1OuW3s\n1qKR/V7q72vA07EPWY6iKmlkzpnPoptqsOSnpgJTXFFo9bup8wzTHw6il2SqM+zU2rKx6w9/VyWZ\nqqqqaG5uZtasWQePXXjhhTz33HPHbA6CIAiCcDITwUUqvbOVqKYQ03Ur8bfuZ3BLHS8ayqkcGsTa\n9Q5pCyop+exV5K5YfMgu2UP9QWSNhCyfeFuhADqjEb7r6GOhwcg3syaXZ9G59Y/4h1rIKJhPTuU5\naA1To2fHZCgJlQ2PdPLSXw9QPiuDm74/D5t9tIJWMJbg/3b30+UJ8fCOXpaXZfHHC+di0H7yFqa9\nL++n5ekm4r5+JElF1tuZdvY85l1RiUaXmq1PvniURs/o1qdgIk6WzsA5OUXMs2Rh0KRmzENJS0sj\nEokAUFBQQH9//8GSxYIgCIIgHDsiuEgR1ecjNNxJf+5CfFd+k8iQi77cPJxXzGe5d5Cq3/2MgoWf\nvDVFUVRUFYpLTsxFdEhR+OpQD3pJ4nd5xegmuR1m5MA2MgrmM/eiXyZphseOqqq4hsIEffGDxxRF\n5flHO9n9rovTVxfwuS9XotON5qLEEgpffX43rx9wAXD+jBx+dd4c9NqP56pEAzHqH9hJ37Y9yPhQ\nFA3G3HIWXFND0eLJBXSHez094QD17mHa/G4UYIYpg1prDuXp5mOaF6TT6YjH42OOXXnllcdsfEEQ\nBEEQxhLBRYq4Hn6GnVmVSGoI+4yZTL/xEnZnT0Pq9HFaWcVhAwsAvy8GwOx5R1cFaKr5iXOAtmiE\ne/NLKZhk34KQe7RMaMYJksCtqirOgTB7d7rZ2zTC3p1uRhyRjz1Oo5W44l8rWbam8OCCXFFVvvtK\nK68fcHHHWZVcVpV/yO7Uw60jNDzYgK+rE1mOgZKOvbqGRTdWk25PTePEmKLQ7Buh3u1gKBomTdaw\n2JbDQls2Nt0Ee5ZMwrp168YEFj/96U/53ve+d8znIQiCIAjCB0RwkSL+V98GYN7XvkHuZfMAeOVv\nbRT5Eyw9t+yIzw8GRoOLvMJjU6ozmZ7yuXnS5+bLtmyWp0/uzstI1zu0vfwzALIrViRjeuPidkZo\nbRyhpd5FR7OHaFQ54nMSMYWgf3TRa7bqmDnfxjmX28jMGZt7kFecTl7xB5+vqqrcuXUfz7YOctvS\naVwxr3DM4xVFofW5Tvb+o4lEaBAAjTGXivPnUXXpDORJVOE6HHcsQoPHSZPHSVhJkKNP4/zcEqos\nmehSNOah+Hw+rFYrqqri9XpZv349jz32GH/+85+56aabjtk8BEEQBEH4ZCK4SAE1HifSfQDSsrGt\nqACgeyRMR0Lhszot9iMkaCfiCk+sbwXAmJ76bsXJ1BoN86PhfpampfO1zJxJnavr3fW4Ot8gHvFS\netqNpNunJ2mWnywSirN3p5uWhhFaGlwMdAUBMGfomLnARrrlyJ+HJEHRNDMz59vIK0k/6m1C99V3\n80BDD1+sLuLWxR80sgu5w9Q/sJPBuhZkKYCiaDEVVlCzrpa8eR/P1UkGVVXZH/RT73HQHvAiAZVm\nG4ts2RSnmY7p1qe+vj6Kisbe6bvqqqt47rnniMVix2wegiAIgiAcmQguUkBt3EFUVdGgQ589ukXl\nqTf7Abj01CPvg+/p8tO8wwlAYUlqqvukgl9J8NXBHiyyht/kFk+q4/LIgXfofudBAGylp1Cy+AtJ\nmWP/gQAD3YExx1QVhnqCtDS46Gzxkoir6PQyFXOtLDmngNkLMymcZk5JYn1CUXmze4RdQz7uequT\nC2bm8L3lM5AkiYGmYXY81ECgfz+ynADM5C5aTO31C0izTq6k7yeJKAl2eV3Uu4dxxSKka7Qszcqj\nJsNOxiTLCE9EcXExvb29Y45t2rSJFSuO/V0sQRAEQRCOTAQXKZB4dRMR2YDOMnpVORaOs2kwQK5J\ny8JK2xGf37PfB8CXvrGAgqITI6FbVVW+7+jnQCzKQwVlZGsn/tWK+B00P3c7ADPOup28OecnZY7t\nO9389/caScQ/XkVIkqC4wsxZl5QwqyaTirlWdPrUVDtKKCrRxOj2ql+92cHDO0YXz6eXZHLnWbPY\n/UQr+17ejRpxABJacz6zPjOfmWvKU7b1yRkNU+8eZpfPRVRRKDCkszavlFlm2yFzPlJp8+bNXHfd\ndXR2dvKd73yHr371qwC0tLSMKTErCIIgCMLUI4KLFFD+uZGQJgtj/ui2oB1bBugwafh8keWotpM4\nh0MAFJeeOP0tHvWOsCHg5ZtZuZxmnPjdlu7tj9C17T4AZqz6Jrmzk9Msz9EX5O6f7iQ738h1365C\nox37OWRk6jGn6G7AhzU7fNzy7E6GAtGDx66tLuKioixcT7by/JOPIsshFEWHpXwWC69bSPbMIwek\nE6GoKh0BL3WeYfYHfWgkidlmG7W2bArTjv0ds6eeeorLL7/84M/bt2/nK1/5Cl/5yleO+VwEQRAE\nQZgYEVwkmXqgC7V7P9HcHOwVucRCcV5oGkYpS2PN7MyjOkeacfRjsdiO/TaUiWgKh/i5c4CV6WZu\ntk4uByDo7EBntFFyyrXkzDo3KXv7A74Yf/5/TUiSxK0/XkBOQWqqKR3OM3sGeKp5gN0OHxa9lm+e\nPh1JAqk/SP7T7bQ7upFlBTQZFC6tpubauehNqcm3CSXiNHldNLiH8cSjmLU6zrTnU51hxzTJyl4T\nce+993LzzTePOSZJkrhLIQiCIAgnIBFcJFli4yaCWiOqpJJRXUZXg5PdZhmbVqb6KEuExuOjW2a0\nh+hrMNV4Egm+PtRDjlbHf+UUIk8yGPAO7MJgyadg/iVJmV88pnDfz3bhGgzzlZ/XHJfA4oGGbn7x\n+j6mZ6aztCST25dOw/ncfg5s3A1xFzFVQm8tZM5l1VScXXrkE07QUCREndtBs2+EuKpSYjSxMruQ\nmWbrpPJjJuvDgYUsy7jdbiyWE+eunSAIgiAIHxDBRZIpr23CmV4CQNaiKjY/1k97kY7ziy1HvYB7\nv4ypRjO1O3Mrqsq3Hb0MxmP8pXAamZrJfZ1UVSHqdyBrknPHRlVV/vrHNtqa3Fz7zTnMmJea7UUf\n5o3EGPB/0NPi7W43v3h9H+fPyOGH80vY+UAT2x97AlmOoCh6bDOrWHTDQqwp2gKXUFX2+j3UuR30\nhANoJYm5lixqbdnkGo59oAVw22238dvf/hYY/Yx6e3uZPn064XD4uMxHEARBEITkEcFFEqleL+r2\nekYsp6K3mAnF0tgRiBCWdZxVePSJ2fXbRnsYHMtynxNxr8fJa0E/P7DnU502+YXqYPMGAOzTl036\nXACvPNnFWy/3c8HV5Zx6Vn5Sznk4rmCUCx55l5Hw2PKoiyzpnPP8fl5/9A0kWUHSZVKyfDHV11Sh\nNaQmaTwQj7HD66TB48Qfj2HV6lmVXcj8jCyMkwwCJ2rdunU89NBDHzteWFgoAgtBEARB+JQQwUUS\nKa9vhUScsFaHubSQzreHaLPrSZMlluYdXYJsLJrANRwmO/f4XFU+Wu+GAvzGNcQFpgy+mHF0uSRH\n0lP/FwAKqz8/6XPt3DbM3x/oYNGKXNZcUz7p8x2NP28/gCcS4+dnzyJNkti/uRtPcw+VnlbiCYk0\nezFzr6ih7IzCI59sgvrCAerdw7T43SRUlfJ0C6tzipluypj0lrXJ+GigbDab8fl8x2k2giAIgiCk\niggukkh5bTMJo5WYHMZYkc+eRid7qy0szTdhPMr8icH+0aZtM2anfgvPRDkTcW4b6qVEp+dnOQWT\nvsOiqgrd76wnHhldbOpNWZM638hwmId/s4fiCjNfuG12yu8AvdU9wgt7h3iqeYDPlNjJ/Esrns4O\niuUohUoa9qr51N5YjSU/NRWY4opCi99NvXuY/kgQvSxTk2FnoS0buz7tyCdIkTPPPBOtVsvGjRsx\nGAxEIhGysrJwOp3HbU6CIAiCIKSWCC6SRI3HUV5/HX9GNRAiIpno1YJLglXj2BL1fnAxryY7RTOd\nnISq8o2hXtxKgnvzS7HIk9/WE/EN0b19dLtM+en/MqlzKQmV9b/cQzymcv3tc1PWq+J9f2vu53uv\ntqKXJPICceb9fRu+eBzZYGfaOfOY//lZyClKzPfGojR6nOzwOgkm4mTpDJybU8TcjCwMSfhcJqqm\npoYdO3aMOSa2PQmCIAjCyUEEF0mi1jeAx8uIOg2MzTgdMvtnpCMBywuOPrh4v1JUSfnUrJbzxxEH\nb4YC/Dy7gNmG5FwVDzo7AJix6lvkVa2Z1Lle+usB2ne6+eK/zyGvOD0Z0zskVVX505ud/K6uixme\nMNe2d6GPSxjzSllwzUKKFuWmbNyecIA6t4M2vwcVmGnKoNaWQ5nRfFzzdCoqKujo6BhzbObMmcdp\nNoIgCIIgHA8iuEgSZeMmVK0OfyIPaMbr1bA3W091hp7stKN/mzv3egDQpyjRdzJeD/r5g3uYS8xW\nLrckb9uWb6gFgMyyJZM6z77dbp5/rJNTVuVx6ll5yZjaITlaXfzkyUZessjUOD1cvs9DYXU1tTcs\nIP0oyw2PV1RJ0OxzU+924IiGSZM1nJKZS63VjlVnSMmY4/XhwKK6uprGxsbjOBtBEARBEI4HEVwk\nifLaZhLTqkkMJABwZ5jpiCX4xji2RAHsa3UDYMuaGgvG9/XHY/z7UC8zdQZ+nD35PIsPU5XR90yX\nPvHE8KAvxoP/1Yw9z8jn/7Uy6VfwFUWh5dkO9m7YyVZLhJfK8znNGebbC+Yw9//NQJZTs/XJHYtQ\n7x6myesioiTI1adxfm4JVZZMdCka82jZ7XZcLhdGo5FgMMjKlSsB2Lhx43GdlyAIgiAIx48ILpJA\n6dyPun8/oYXnoprDoMDAgtHSp6uKxhdcdO8fTWqeSmVoY6rKbYM9RFWV/84rxpjkRe1w26toDRkT\nfs2qqvLo71rxuKL8+69rMaYn72sdcoepv6+JwYZWZCnAiNbAC6XlLLFbeOArK9DIyf+cVFVlf9BH\nnWeYfQEvMlBptrHIlk1Rmum4fzcsFgt+v//gz6FQCBBBhSAIgiAIIrhICmXjJgA87jn4N2odAAAg\nAElEQVREs/egOvW0ZBmYhso0y9HfgQiHRpvnTbV8i1+7hqiLhLgrt4jp+uTeUQk4O4n4B5F1E99O\n9NJfD7DjTQefvbGCssqMpMxrYIeDxocaCA4cQJYTKJIZ6ZRF1JWnQbeLn62tSnpgEUkk2OVzUe8e\nxhWLkK7RcnpWHjVWOxZtchoLTtZHA5trr72W9evXH6fZCIIgCIIw1YjgIgmUjZtgxkxCPWbCM/yE\nMmzs8Ee4tnJ8JVWdjtErwAtPS00y8ES8EvBxn8fJNRmZrDVbk3puVUnQ+PgNAFSsuG1C56jfMsRz\nD43mWZx9acmk5qPEFXY/1ca+l3ahRocBCa05n7y1c/l1yEvjgBf2B/jyKWWUWJOXW+GMhql3D7PL\n6yKqKhSmpbM2q5RZZhva47z1yefzkZOTQ0dHB4WFH/Tn+Pa3v82dd955HGcmCIIgCMJUJIKLSVI9\nHtS6BuLnXU3YDUrQzb7aecTV8ZWgBejqHN0SlVeQmn4I49UVi/JtRy/zDWl81578BOlo0AWAKXsm\n2RUrxv38/S0eHv7NHqZXWbn63ybez8LvCFF/bwPDu/YS0Ee5f2YJ0TQ7+gwDGp3MSG8/MUXlu2dW\nUGo1srLcPqFxPkxRVfYFvNS5HRwI+dFIEnPMNmptORSkpa7K1dHy+XzYbDYUZbR6WVFREaqqoqrq\ncZ6ZIAiCIAhTmQguJknZshUSCYLpNbiLJTRDHvbMnEGWQcOCcVYOikRGE5uLSscXlKRCRFH4+mAP\nEvC73GIMUnKvoDs7tuJo+ycAeVUXII9z249rMMz/3rELa5aem38wD51u/PPrfnuAnX9pIDzcjSwr\noLGybdFMBtQoaypzeT9U0coyX6wuYm7u5LerhRJxmrwuGtzDeOJRLFody+0FVGfYSdce/z9Hn8+H\n1Wr9WBBxzz33HKcZCYIgCIJwIjn+q5kTnLJxE2Tb8Qzk488bwTgYpzE9k/MLzWjGeSXd54kCYLUd\n/0pRP3cNsisa5n/ySijRJXe/f8TvoOWFHyDJOvTmXGylp47r+aFgnP/5cRPxqMLXf1GDxXr080vE\nEux4tIUDm3YjJVyoioTeWkjV5TUYFufwg4e2cfncAu44a9Z4X9ZhDUaC1LuHafaNEFdVSo1mVmUX\nMtNsRZ5Cyfutra1jAovnn3+eCy644DjOSBAEQRCEE4kILiZBjcVQtmxFPuccBptDqHoP7UWlBCWZ\ns8a5JQpgsD8AgF5/fPfZP+v38Jh3hJusds42JT+5vPWlHwNQsvgLlJxy7biem0goPHjnbga6gnz5\njgXklx7dFjJPr5/6exsYaWtHliOoigFrZRWLbqjFWmLGEYhw49+bkCWJfz2lbNyv6ZBzVVXa/G7q\n3cP0hAPoJJl5lixqbdnkGFLTD2O8tm/fzimnnAKM3p246aabKCkp4eGHH2bFivFvVRMEQRAE4eQm\ngotJUOsawOcjWrkE1wgYCbFr+kzSZFiSN/68if6eAJIEGu3xCy72RSP8wNHH4jQj38hKfmK5b6AZ\n38BuTDmVFNVeNa7nduzx8Nz6Dtqa3Fz5lUpmLzxywnzn5m52P7GDmLsPSVaQdJmUrjiF8stm8s3X\nWviXp7cDoALpOpk/rZ1HvmVyncf98Rg7PE4aPU78iRg2nZ5V2YUsyMgiTTM1/uQ2b958sC/F+378\n4x9z00030dXVdXwmJQiCIAjCCW9qrHROUMrGTaDT0eosI5IRoChDYmfRDJbmGEnTjD9AsGToMZl1\nyZ/oUQoqCl8d7CFNkrkrtxhdkrfrOPdtoevd0bKl05d/DVlzdK/1QJuXDY900rzdhdmq44p/rWTZ\nmqJPfHwsFKPxoWZ63mhGUj0oikwkt4TXF+cRt2gBhX1/a2DAH2FdTTEm/Wg39PMqcpiTM7F8F1VV\n6QsHqfcM0+Jzo6AyLd3C+bZipqdPvIdHKpxzzjm8+uqrY4719vaOqQYlCIIgCIIwESK4mCBVVVE2\nbsJ3+vl0DgXIGJEYKNXgtlg5q2RiJVs79rqpqLQleaZHR1VVfjTcT3sswgP5peRrkxfkKIkYjrZX\n6dn+MPGIn5zKc7DkVR3xed37fGx4pJNd25yYMnRcfP10ln+mGEOa5pCPd+3zUP9AA97ODmQ5iqIY\nyZ67gOKrqrh1SytDHj8VmtFKTDkmPT85q5IzSsdXLvij4opCi99NndvBQCSEXpZZaLNTa80mSz+5\nOyDJ9Ic//IHbbruNWCzG008/TUbGaMDj8XiwWKZWXxVBEARBEE5cIriYILWjk0RXLw1LvoPGBxU2\nCw8qBiRVYcUE8i2ikQTRiEIwEE/BbI/sSZ+bp/0evpaZwxnpya1W5ep8g/bXRnsiTF/+NQrmX3LY\nx/d2+nn+0U52vDmM0axl7bXTWHlRMWmH6LytKArtL3XR+swO4oEBJElFNmQz/dx5zPtcJb54gque\nbMAZirL+kmpqCpLTq8Mbi9LocdLoHSaUSGDXGzg3p5i5GZkY5EMHP8fDz372M77//e8f/HndunWs\nX79elJQVBEEQBCElRHAxQcrGTeytXIsnZKBoZwLrOivbI3YqAx6yDON/Wwf6RpO5FyzKSfZUj2hP\nJMyPnQOcbjTxZVt20s/fvvFXANR+4RGM1k/eztR/IMDzj3bSsNVBWrqGNdeUs+qzJRhNH38/I/4o\nDQ/spO+dFmR8KIqG9LxyFlxbS2HN6Hs4Eopx07NNHHCHuO/iBZMOLFRVpTvkp84zzF6/B4AZJiu1\ntmzKjOYptfXptttu47e//e2YY1qtVnTTFgRBEAQhpURwMUGurc3srbyMggILlpfd7Bl4m+7yKq4P\nD477XKqq8j+/3gFAcdmx7XHhUxJ8dbAHm6zhN7lF4y6feyTegd0kogHSbCWfGFgoisrT97az6e89\n6NM0rL6ijLMuLcFk+fjWLMceFw0PNuDv6USW46CayK5ZSO0N1aRnjW5DenJ3Pw829uAIRAnE4vx+\nzVyWlGRO+DVElQTNvhHq3cM4omHSZA2nZuay0GrHqjv+ZYMP5dFHHz34/waDgXA4fBxnIwiCIAjC\nyUIEFxMQd7hoMJyOQROnNJGGVy+xJRgE4MKlleM+XzSi4BoeXfxVVk18ETxeqqryH44+euJRHiks\nx56CSkb9TU8DUH76lw75+0RC4bHftbLtlQGWrSlk7RenYf5I3wpFUWj5ewd7N+xEeS940xjzmHnh\nfGZfPJ26fi+/390DgDMU4+8tg8zLtXB6aSZXzy/klKKJ5bGMRCM0eIZp8rqIKAnyDEYuyC1hjiUT\nnXx8ywV/1Nq1a9mwYQMw+rkODQ0xZ84c9uzZc5xnJgiCIAjCyUQEFxPQ8/ft+DKKWHKmnuj/BTBW\nmWnIKaQoHGD2jNnjPl9bswuAz3yuAmP6sasW9ZDXxYsBH7dn5bI4LT0lY4Q9vQBklp72sd8l4grr\nf7mH+teHuPAL5Zx/VfmYrUVBV5j6+3cw1NiGLAVQFC3m4pmUXz0fZ5YOD/BwUx93bt2HBOg0o8/9\n3NwCfrRyJroJVOxSVZXOoI969zD7gl5kYJbZRq0tm6I005Ta+gRw5plnsnXr1jHHWltbmTVrlggs\nBEEQBEE45kRwMQH794QxRhPkn3UOrT/ajv6afNrsGi7y9E3ofG9uGn3evBp7Mqd5WI3hIHc6Bzk7\n3cyN1tSNGw97MWaWIX/krkgsmuC+X+xm1zYnn72xgnMuKz34u776IZoeaSA42IUsJ0CykH/qKSy8\nfj4t/jBffHYnI+HYwcefXpLJH9bMxTyBXJf3RRIJdnpd1HuGGYlFMGm0nJGVR7U1G0sSK2cl00cD\nnaKiInp6eo7TbARBEARBEERwMW7+AT/Dch6zTa1E28OoMZUds/QogQinxbwTOufePSMAlE7PSOZU\nP9FIIs7XBnvI0+q4M6copVfjVVUh/SO5FtFwgrvv2ElLwwif/3Ily9cWocQVdv1fGx3/3AWxYVRV\nQmfJZ9bF1cxYXYosy3R7Qqx7upEso55fnjcbo06DVpaYn2dBO8FtSsORMPUeB7u8I8RUhcK0dJbZ\ny5hltqKRptbWJ4CKigpcLhcjIyOsXr2al156iZkzZ9LW1na8pyYIgiAIgiCCi/Hav2EXqCplZ5QQ\nbPQB0GhSMLrCzDGPvwSpoqj4fTEyswzHZMuNoqp8a6iP4USCvxaVY9Wkpmxq9/aH8Tv2Eg04sE8/\n8+Bx70iU+36+i449Hq75t9nMr7ay+edvMrx7Lxo5hKLoyJg2m9rrF5JVMba606NNvUQTKg9dWk1x\nhnHCc1NUlfaAh3r3MAdCfjSSxBxzJots2eSnaHvYZBUUFDAwMDDm2IsvvnicZiMIgiAIgnBoIrgY\nB1VROdAaIWd4H+azr2Tkh93oCg3UeUNU9HZjWjD+7UVuVwSA8hnJ6b9wJHe7nWwO+fmRPZ/5hokv\n0D9J2NuPu6eerm0PACqm7Blklo3mW+ypd/HQr5oJBxOsvbiAkecaeXl9N7KsIGusFJ1RQ821VeiM\nH9+GFIwleLJ5gHMrsiccWIQScXZ4nDR4hvHGY1i0OlbYC1iQYSddOzX/FLKzs3E6nWOOnX322cdp\nNoIgCIIgCIc3NVdUU9RAm5uQmsbc9CEwGgk2+ogstdAdVqjt6ybtvBnjPmc0mgCg9rTcZE/3Y7aF\nAtw1MsSFpgyuzkhNVaq9r/4n3r4mAMpPv5WihVcQjyk8c187rzzVjS1DQ63ZQWjLHlRFxmArZM7l\n1UxfVXLY8/6jdRBvJM4Xqz+5T8YnGQwHqfMMs8c3QlxVKTWaOSuniJkmK/IUS9B+n8/nw2KxjAks\nLr74Yp555pnjOCtBEARBEITDE8HFOOzfuB99xEfhGdOIDUSJO6K0zk0DQszo7cI8/fAL5EPp7/ED\nH0/OTTZHPM5tQ72U6/T8NKcgJeMNtbyEt6+JzLLTqFj1TfTpdhx9Qe69Yye9B4IU6n3M0vYjqXqs\nM+dSe9NCrEVH7usx4Avz53cPMDvbxKKjbISXUFVa/W7q3cP0hgPoJJl5GVnUWrPJScEdm2RJS0sj\nEhm9m6WqKvfccw/vvPMOd99993GemSAIgiAIwpGJ4OIoRYNx+jrCTOt5C91/3IynYTTfYneOTNpg\nlApNAp11/A3wnn6sHYDsvNQteBOqyjeGevApCR4sKMUsJz/PwtH2KvvfugcA+7xb/j97dx4fVX3v\nf/z1ncks2fc9gbCEJWHfBBUBBUTcWqWt28+lWr0obr23y61X7dW21qWVWqsV11qX61ovVbS3VVBR\nRAgEAoQlISEkIXsymclMMsv5/v6YGAgECCEkkXyejwcPmXPOfM/nhCPMe77n+/1SsCHAxg83UbjN\ngcJgfEQVSWFWhs49m4lXj8FsOX4NL28p553tVVS52vAZBk8szj1uKHL5feQ76tniqMMV8BNjsXJu\nQhrjo+Kwn4J1PHqD0+kkNjaWQCBwxL6bbrqJm266qR+qEkIIIYQ4cQPz09YA1FTZgsZEsr0JlZKC\nZ2sJym5is8/HiJoDJE3P7VG7DXUeANIzT93K3H9orOWrVjcPJ6Yxymrv1bZbHZVU7/yIA4XryNs8\ng9qm8bz+wT4AQlSA+BA3uekWZlw7n8yZKd1ut7DWxW8+K2JUfDhnZMRw09RMJiR3PZuW1prKVjd5\nTbXscjkw0AwPi2RRTCLDwyIH3NoUh4uK6nxdjz76KP/xH//RT9UIIYQQQvSchItucu0PThcbMW00\nAO4tzXinRLDX6eXislJCxvRsrQi/XzP/wqFYrKdm1qZP3U6ebqrje5ExXBbZs5Wqj6V802tUbf+A\ntXk/oLx6FLEhHkaG1RJjDpA1cQjTfnQWEYnH75XRWlNY58LjMwB49Itiom0WXr5sEjH2rteZ8BsG\nha5G8prqqG7zYDWZmBKTwOToBOKstl69zt7kdDqJjo7mpptuYsWKFSilOh6Bkl4KIYQQQnybSbjo\nJtfWYpRhJ/y8WRieAJ5dbvbckgx4Gbl/H5YZQ47bxuFaPX4APG7fcY7smQN+Hz+pqWSM1cZ98d3v\nNeguwzCoyt9HQf73Ka8dzcjQWrKiTYw4fwK5l4/CFNK9dSIMrXlgzR5eK+i8COFvzhvdZbBo9nnZ\n7Khji6MejxEg3mpnYWIGuVGxWE/BI1+9pbKykvT0gwPSn332WVasWIFhGP1YlRBCCCFE75Fw0U0t\n+xoI9YVhHj8L92Yn+DU70syEtkFmbVWPxluUlQQX3UtM6f21Fbxac0d1OV6t+WNyBvYeLjLXlbZm\nL5teKuDAhkJa/FModAwhMdzge/85l/Qp3Zv1auXOah74dA+G1mgNLb4A105MZ96wYA9QtN3CuKTI\njuO11pR5XGxqqmNPiwOA7PBopsQkMCQ0YkA/+uR0Oo949AlgzZo1fV+MEEIIIcQpJOGiG7TXR4tL\nEx4LymTCvSU4mHuLOcC4MIXZMLBEnXi42Ls7+CF5SFbvr8z9WEM1+W0enkjKIMvSO48IVe9oIP+l\nTbRUlGIy+fEbEWxvi8Ue5uPHz51LZLS1W+20+gM88kUxiWFWZg+NAyA7PpwlOSlHhASvEWB7cyOb\nHHXUeVsJNZk5IzaJydEJRFm6d77+8s10spGRkZ2279y5k9GjR/dTVUIIIYQQp46Ei27QGzbQYo8j\nPT04dsC9xUlbtp09Li83xQRn+OlJuNizMziOY+iI3g0X/2hp5kVHA9dGxXFBxMm1bRgGhX8rpujD\nAozWGgDMYckMOy+RdducuDbZuPQ7m4mMXtTtNl/ZUkFNi5ffn5/DjIyux4E0etvY5KijoLmeNsMg\n2RbK4uRMxkTEYunFXphT4Z133mHJkiUAjBkzhsLCQq699loeeugh0tLS+rk6IYQQQohTR8JFN7R9\n8jk+21wixqagtca9xUnxhTGAQa6/BQBLVPgJt7s9P7hAWmhY7/0x7PN5+XlNJRNsdn4Wn9zjdtwN\nrWx6fgs1+bswmdwYRggRmdlMvmEyiaPjeOPB/6Zg0zmMy/6UKfPnd7vdN7dV8vt1JZwzNO6IYKG1\nZq/byaamWva6nZiA0RExTI1JJM0eNqAffQJ48sknuf322zttq6qqAuAvf/lLf5QkhBBCCNGnJFwc\nh9Ya51c7IGcuEcmRePe3Emj0U5hlwWbyMrzFQSmc8JiLz/9VDsC5FwzBZOqdD82thsHt1eWYFTyR\nlIG1Bx/GK/Jq2PrqZjzVZZhMATBFkjJjBpNvGIctIvgYUtnuOtZ9PZOU5Gque2ApodHHDzEBQ/PY\nl3t5ftN+Zg+J5fFFOR372gIBtjbXs9lRR6PPS7g5hLPiUpgUHU9ESNczRQ00r776aqdgYTabaWxs\nPOKRKCGEEEKI05mEi+PQe4poCXZOEB5vw70pON5ia6jBxIhQVElwZ0jEifVcNNa3ArDo0qxeq/VX\n9VUUeltZkZJJ+gmMRzD8BgVv7KLk423gq0drhSUylTHfncCIBUMwHfIYksvh5c/3b8QS4uWy/2cc\nN1iUNXl48LM9lDk8lDR6uHpCGvecM5IQk4naNg+bHHVsb27Epw3S7eGcHZ/K6IhozGpgP/oEcPfd\nd7N8+XK01lx99dVcc801WCwWvF5vf5cmhBBCCNEvJFwch7F6De6wBAAi4mzUbqnCE29mt8fH0mHR\n+La4CIkIxRRyYlOgFrcP5o6K6Z3B1v/rbOINZxO3xMQzL6x735a7qt3kPZdP/Y7dmEytGIaV6OFj\nmHzDZOKGRx9xfMBv8PxvCnA5zcyf9QYjZiw/4pjP9zWQXxWcBStgaF4rqEBrmJASxQ8nZ/K93FSK\nWhzkNdVR5nFhVoqcyFimRCeQYu/9WbNOhauvvprXXnut43V6ejoVFRVorfuxKiGEEEKI/ifh4jiM\n1WtwZ8zBGhaCJTQE91Yne2dFoIFpiaH4HK4THsyttWZPYWOv1bjH28q9dQeYYQ/jrtjjTwW774tK\ntr+RT2t9OSaTgTLHkD57MpP+Xy4W+9FviXeeLWJPQTMzJ77P6OljsEUkAuBq87O3yc368iYe/WJv\np/eMTgjnycW5JEZa2eqo55nSHTT7fUSFWJgTn8qE6HjCzN+O2/DWW2/l6aef7rQtOjqaioqKfqpI\nCCGEEGJg+XZ8qusnur4enb8V9/euJzzeRqAlQOseN7suTMJiCjAhPpTC5hMPF/kbajEMzcRpiSdd\nY0v7OIswk4nHk9IJOco4C39bgC2v7qDssx2oQCPaMGGLTSfnexMYNifzuOf58qNKPvt7BaOHrWNY\nxlaGznwdCAaLS17fSHlz8DGvBSMS+P35Y7Gag481VbW62eSoo7CukYDWDA2N4LzEdEaGR2Ma4AO0\nv7Fr1y5Gjx7dafxEYmIiNTU1/ViVEEIIIcTAM+DDhVJqEfAHwAw8p7X+7WH7fwzcBPiBWuCHWut9\nvXFu49PPQWvclmhi42x4tjnBgIJomBBtx2424WtuOeGZovYVBx+JWvzdYSdVn9aa++oOUOLz8lLq\nUJK6GPzsKHOS9/xmmoqKMJm8GIaN+NHjmHzjJKLTuxeKirc38cZTuxk9KYpJqf8iYeQ87FHBFb8f\n/qKYSmcrvzlvNBlRdqanx4DSFLqayGuqpbLVjUWZmBAVx5ToRBJs9pO65r40btw4tm/fDkBFRQUP\nP/wwQMd/hRBCCCFEZwM6XCilzMCfgAVAObBBKbVSa73jkMM2A9O01m6l1FLgEeAHvXF+Y/UajORU\n3C2QEW/Hne/EY4Pdfj83JQbHJLQeqCUye+gJtWu1B8dnpA05uZmE/sfZxEqXg7tiE5kV2jngFH9c\nRuHbW/A5K1FKY7LGMXReLhOuHIPZ0v3xIY21rTz3623EJdlZdMFOarZqYoZMB+DL/Y28se0AN03J\nZEluKi6/j3WNVeQ76mkJ+Im12DgvIZ3xUXHYzCc2JqU/DRs2jNLS0k7bXnzxRe655x4JFkIIIYQQ\nxzCgwwUwAyjSWu8FUEr9D3Ap0BEutNarDzn+K+Ca3jix9nox1n6J98Il6DZNRLwN9/t17J0eTkDD\ntMQwvE1OfA4X4UNSut1uIGCQvyH4OE1ISM8fC9re5uHBuirOCQ1naUxwwLnP42PzS9soX1eISTdj\nGCbsiUOYcNVkMmZ0v8ZveFsDrHiwAF+bwZ2/HU/l2j8DEJE0Br9h8OCaPQyNDuXyiUmsPFDKLlcT\nBjA8LIqpMQkMC4sc8GtTHM5qteLz+TpeT58+na+//rofKxJCCCGE+PYY6OEiHdh/yOty4IxjHH8j\n8GFvnFiv3wBuN+5Js2A9hMXaqN/iZPfVsYQog4nxoXh2Bgcvh2V2/4P77h2NlO0NTmfb0w/ezYEA\nt1eXE28282hSOo3FDja9sBnnvr2YTD4wQokbP5GpN04iPDG0R+fQWvPqH3ZSXuzi5vvGk5hiosII\nED9iHjoyk79uqaC40c2SM2J5u6oYm8nElJhEpkQnEGvtnRmw+kpsbCwOhwPDMPjnP//J3LlzOe+8\n8/jXv/7V36UJIYQQQnyrDPRw0W1KqWuAacCco+y/GbgZYMiQIcdtL7B6DdjteFKGA+VY3BrDGWB7\noiI3LpSwEBNVlcEeiND048/Q9I0v11QCcPe9U7v9nkNprfl5bSUH/D4eKrGy9tfvE3BXAxqTPZER\nC8eRe3k2ppCTWyfiX2+XkfdpDRdfN5x4+xq+evZPtGoLD3u/R/6f1wKQGhfC8GQr02ITyYmMxWr6\n9jz6BBAREUHLN4uYAE6nkzlz5siUskIIIYQQPTTQw0UFcOhURhnt2zpRSs0H7gHmaK3bumpIa70C\nWAEwbdq0Y3561FpjrF6D6cxZtDQHUGaFLmql1QKFBLi+vTfAc6AWAHtKQrcvyGwO9lZkj43t9nsO\n9WxNDf90O7now3L4ai9+w0x46nAm/r9JpEw8+dmnALZ9Xcdf391L1QIrL5nqaf60BX/IUhqtSThc\nMHG4jczIMK4fl8n4uNhv3aNPMTExOByOTtuWLl0qq2kLIYQQQpykgR4uNgDZSqlhBEPFFcBVhx6g\nlJoMPAMs0lr3ytygetduqDyA6dZbqC91EZ0SSmuBi+LRNgIapicGF3vzVNZiS4jBbOveatiGofl6\nbRWJKaGYTCf2gbx6Wz3vfrSN5ZfGM25XA2evqyZx6lSm3DCe0Jjem4GpqqyFFx/Zzu4zzARCA9g8\njXiiMwiE2IhUiqunJ3JDbhZRJ7AC+EDgdDpZu3YtF1xwAS6Xq2P7T3/6UxmkLYQQQgjRSwZ0uNBa\n+5VSy4B/EJyK9gWt9Xal1APARq31SuBRIAJ4q/0b9DKt9SUnc15j9RoAAjNnU/9kKaPnpuFeUcee\ns8IwK5gcf7Dnwp7W/Ueiyvc5MQyNonvBwjAMdrxbRPFHBThNjTy/dCqxzX7uIY0pL5+NyXRyjz4d\nqt7tpbS6hb/+rpDyTEWDzWDeaBvDM6KIaN7PtLhopg6dQkgvnrMvOJ1OYmJiMAwDCPZKNTY28pe/\n/IVly5b1c3VCCCGEEKeXAR0uALTWq4BVh22775Dfz+/tcxqrP0WNH0dtkwVtQGJGBLV797P9++GM\njbES3j6Vq6eyltjJY7rdrrctAMAVNxz7Pe56D3nPb6F2y25MJjd+beGtH03FE2XjzYxh5E7t2SDt\nI87j8lG0rYnX8yt5r7WRgAkYFdwXF2lmhmkHaes/Z+EVz2LuYg2NgczpdBIdHd3l+InIyEgJFkII\nIYQQp8CADxd9TdfVobcWYL79Nqp2OQixmbA3arwhsDPE4Or2R6JKX3mftpoGQlO7P87B7w9+0LVY\nu/72v2JjNVtfzcdTU4bJFABTJKkzZ7B+STo7Wup5MCGVXFvPg8WWqmbu+XgXdc1ttLUG8PsMtAKv\nTZHYqhgZaiZimJVRQ2KYWPIylm1fkDBy3rcuWABERUV1er1q1SouuOCCfqpGCCGEEGJwkHBxGGPN\nZ6A1at4cqt9rImlkNK0FLvZkhOAjuL4FQP36AgBSzj+r223XVrkBMB+yvkXAF1gBMQsAACAASURB\nVGDbG7sp+Xgb+OvRWmGJSmXsZZMYuWAI6zwt/OnAPi6JiOYHkTE9vq5PS+q47f3tWNsgutog2m4i\nNNmGL0ITl2JmYU4c02ITGRURg7u6kK1ffIEpxM6QM27o8Tn70saNG5k+fTqJiYnU1NSQlpZGZWUl\nGzZsYNq0af1dnhBCCCHEoCDh4jDG6jWQmoIrfijuxgLGzEvD/WIdeyaHoYApCaH43a00bdlF0txp\nhJ3ANLS11R4AoqJtOKtayHsun/rCPZhNrRiGlejhY5ly42Ris4Lfulf7fdxdU84wi5UHElJ7NCtT\nIGCw+oMK/mN3MdY2zfxyG+nnhuMZb2AJMZETGcuU6ASS7WEH6ywKrkuYe+ljhMZkHq3pAeHDDz9k\n8eLFHa9ra4MzeFVUHDGpmBBCCCGEOMUkXBxCt7VhfLEO03cuoa59obvEEVHsLyih8KxYxsRYibKa\n2ff2PwAITe1+sACoKHMSQRtfPfAJvsYKlMnAFBJDxuwpTLwmB4v94B+HX2vurqnAbRj8NTWL8B4M\npPZ5A7z48A7+90Ad7jGKxZnhxC2xYLWGMDM6gQnR8YSaj7wFWh3BD+ZRKbknfM6+1FXYklAhhBBC\nCNF/JFwcwvjqa/B4MM2bi+OAG4vdjLkhQFubQWGY5gftj0R5G4JrJAz/0WXdatfX6mfrq4WEb9vK\n2ZZWfI0mrLHp5H5/ElnnpHf5nuWNNWxodfNYYhrZPVjx2uP28+Qvt7C5opnSMxQZiSHMPCuOqTGJ\njAiPwnSUXhCtNY2l6wZsj8Vzzz3HD37wg05rUiilcDgcsk6FEEIIIUQ/k3BxCGP1GggLxTRzBo7n\ni4lKCaV1q4vitBC8HBxv4XO5sSXGYgo59o+vcV8zm5/Pp6m4CJPJS6gKodyawrUPzicqLeKo7/uk\nxckzTfX8IDKGS09wnEVAG2yuqOOtX+1mt+Gj8AxFYpSFPywcx/j46OO/3xd8dCsiafQJnfdUu+++\n+3jwwQcBuOWWWwgEAqxatYqzzz5bQoUQQgghxAAh4aKd1hpjzaeYzpwFVivNB9xkTorHvcnJrjHB\nnoOpCe3hoqkZS/TRw0Hxv8rY8c4W/M5KlNKYrPFknZfLXz6uYubstGMGiwqfl5/WVpBjtXNvfEq3\n63f6feQ76thYWkPpHxvZGacpyVLMyozhj4tzibIdf8YnrTWVW94CICp9UrfPfSrdeuutPP300522\nhYUF/xxk9ichhBBCiIFFwkU7vXMXHKjCtOxWPA4vvtYA0alhuLdWsPPiMLKjbcTYgutbtNU7sMV3\n7lHwtvjY/JdtVKwrxEQzhmEiNHEo46+eRMb0YEj48/sVhFiOPnbCqzV31JQT0PBEcga244yz0FpT\n0dpCXlMdu11NtFUHaPxzCzXhUJKl+H5uKvfPzcZiPv54DZ+nieaq7ez/+iWAAfFYlNPp7BQswsLC\naGlp6ceKhBBCCCHEsUi4aGd8sgaUwjT3HBwHgo8GhYdZqCpvpTAmjMsSD64v4a1vImJE8MN33Z4m\nNr+wCWdZCSaTD22EEj9hElN+OJHwQ95TUhQcp6GNIxd1+8Zv66vZ2tbKk8kZDLVYj3qczzDY4Wxk\nk6OOmjYPNpOZiaHxfPpsGQpF6RQLYyNt/HJedrdW1Db8Xjb+9SqM9keixn13OdFpE477vlNh/vz5\nfPzxxx3rUiiliIqKoqmpqV/qEUIIIYQQ3Sfhop2xeg1qwnhUQgKOgkoALLUBSlLNtKqD4y10wKCt\nsZlmh4WVN79HwF0FgNmeyIhF48m5fCSmLj7Qf7kmOItRzoSELs//oauZvzY3cEN0HOeHR3V5jMPX\nxiZHPVsd9bQaARKtds5PyiQnMoZP3tyPo9ZL6E2JNJTX8edLx3crWAC0OqswfB5SJ1xGTOZ0olL7\nPljMmjWLr776quP1xRdfjN/vxzCMPq9FCCGEEEL0jIQLQNfUogu2Yb7rdgCaD7gJjbbg2+6mMCs4\nVmFqQhitDi9f/+5jWpJycZa7MQwv4WkjmHjtZFLGdx0avrF7eyMAI8ceOUC7xNvGf9ZWMtkWyk/i\nkjvXpjX7PC42NdVS1NIMwKiIaKbGJJJhD0cpRXNDG/98q4zks2J4taKOK8enMTGl64DSlTZnNQDx\nI+b0eY/Fk08+ye23395pW2ZmJmVlZX1ahxBCCCGEOHkSLgDj008BMM2bC4CjykNUchjudU52TbYz\nLuBn472raanch8nkxwTEZ49g+p1zsEcf/fGljvYNTfUBNyEhCqvV3Glfq2FwR005VqX4Q3IGlvYp\nYtuMANubG8hrqqPB10aYOYSZsclMio4n6rBHpt5+ZS+VUQZFQ/zEtlr48ZnDTuj6G0rWAmCP7P4A\n8pP1s5/9jIcffpjrrruuI1yMHTuWHTt29FkNQgghhBCid0m4AIxPPoW0VNToURgBjbPWQ8LwCIpL\nysmJaWTU1/W4NYSEp5Cc1obzk68486VlmEOPHywAincFxwuMHhd3xL7/rq9il7eN51KGkBpiocHb\nyqamOgqcDXgNg1RbGBcmD2FMREyXjzl9uqWG5Z4qWicraGzh9+eP7dbMUN/wt7mo2rYSAGt4fLff\n11PJycnU1NQA0NjYyIoVK9i5cyejRw+sqW+FEEIIIcSJG/ThQre2Yny5DtPl30UpRc2eBtrK6thb\nWog5w0O6w0Lz0BFceN10ErJjWLPo31CAyd69YAGwY2s9AOdfktVp+zvOJt52NrE0Jp40I8CbFcWU\nuJ2YUIyNjGFKTAJp9vCjtlvU0MKta3ZgNin+uCCH0akRZMWEndD1b379BgCGzb4dZTIf5+iei4mJ\nweFwdLlPgoUQQgghxOlh0IcL46v10NpK1ZBZbLvrIzy1+wk1BQgYkey1juAvC9L516WjSAgNQWuN\nDhhEZA9BHWWF68PV1Xj48G8lAAzLPjjeYpe3lV/WHSAnxEKoo553/F4izBZmx6cwMSqe8JBj9z74\nAga3/20b+DT3DR3K+WOTTvzaAz68LXWYQuwkj118wu/vrmHDhnUKFpdffjlvv/32KTufEEIIIYTo\nH4M+XOx+o4CdOXeiPt6N1gosSbRaYpgZm8bfk3xkRNtIDA3+mPzNLRhtXlLPP6vb7RftDA7kHp0b\nS0hIMJCUelr4YdV+TIbBJH8LUfZw5iWkkR0RjbmboeVP60spbvEwu9LKpXdmndhFt/M07gMgbeLl\nmC32HrVxNDabDa/Xi9aakpISlFIsXbqUp556qlfPI4QQQgghBo5BHy4K68MgNED0sBwm3ziJXZ9X\n07i/BfdnTnZeG8H5iQcfM2opOwBAaOqxZ4b6htaapsY2AK67NZddLgd5TTW83NZGrTKxLDSMK+JT\nSLKFHqelzvY7PDyzsYzUSs2yy7KxHGNhvmNpbZ8lKiKpdx5LcjqdxMbGEggEOrY9+eSTLFu2DK2P\nvr6HEEIIIYQ4PQz6cIE5gMUI5dwHzgGg+a1SImNt7A204Qo5uL4FwM7HXgIgND25q5aOsPqj/bz3\nehEoeLWmCHdIgBKzlb0mM3fGJLAs7sQfZQJ48INd6ADMJ5JJZyX2qA0Awx8MPqExQ3rcxjfi4+Np\naGjotO2Pf/wjy5YtO+m2hRBCCCHEt0PPvvI+TRhOF8ocwGwJjm8w/AbO2lbshokdQ4K569Bw4XO4\nCMtMIXxY+nHbPtDq5uMvg2s1pFwfTVJUGOMT0vkMxZzQCG6NPfFQoLXmjy8Xsqa2kVyHhdt+PK7b\nYz+64qreCYDZdvRB48dSWVnJ1VdfDUBWVlbH9ldeeQWttQQLIYQQQohBZlD3XLj37EcpCLHbAGg6\n4EYbGluDQeEwC+lhFlLD2oOH14e3wUH6pfOO+oHebxjscjWR56jjQKub+mIPALfPm0CI2cJ3KvaS\nGBLCo0lpmE4gFHgDBi9sKGPN51Xkmz3Em0L4891TiYvu2TiJyi1v0+qsprlyKwDWsBObgnbXrl2M\nGTOm4/Wrr75KXl4eTqeTyMjIHtUkhBBCCCG+/QZ1uKjZFFxvwRwW/Oa+ocwFgGlPGzsX2jgv6WCv\nRWt1cDpZe/KRH8Sdfi+bHfVscdTjDviJs9iYE5lCiVFLfKKdOIuNf6veT7Xfx2tpWcSau/9jr3d7\nueVvW9la7yLEBFlhdl6+ZgqJEbYeXXObq5aStX/CZLaizBZis2Z1u/dj48aNTJ8+/Yjt34QKCRZC\nCCGEEIPboA4XLRXBmZzisqMBaNjnwh5poaygDafVzrTEgwOtG7fsAsCWGAsEH1Eqb20hr6mO3a4m\nNDAyPIop0YlkhUXgbvEDcO4FQ3nOUc8nbhf/FZ/MJHv316HYXuPkR3/bSqPbx/QiE/f9aDyjJ8We\n1DVX71gFwOhFvyQua9YJvffwYCGL3wkhhBBCiEMN6nARcAbXXggbkog2NHUlTqJj7HydFpzZqNN4\niyYnAOE5I9jiqGdTUy013lbsJjPTYxKZHJNAjCXYm1Bb7eahX6wHoCQywO8aarggPIpro45coftQ\nO2qcLH1/G7VuLwB+Q2P3wAVVdn5x70QSUk5sVqnDaSPA/g0vARA79IzjHv/qq69yzTXXoJTCMAx+\n+tOf8sgjj1BRUUFaWtpJ1SKEEEIIIU4/gzpcuBqCH+KtsZHs31KPu8lLVmQEhUN9pNjMpLePt/C7\n3BSveBtCbTxTVUSrESDRamdRUiY5kbFYTJ3Hxb/9ym7cLX4mLkzjhQwvGWYrv05MPebjR3sb3Vz/\n3hbCLGZunJzBzs2NVOx2cm5CLLf9ehyhYT3/o9JGgOJPH8fbUgdAVOoElDr6WP4nn3yS22+//eD7\n26eRffjhh3n44Yd7XIcQQgghhDi9Depw4Wkx0BaFJczC5pVFxKSFEVbqp3CSldnJwXEYJS1OCj7+\nnHCgcUwGQ8MimRqTQIY9vMuwEAgYbNlQi1bwxfwwmgOtPJ88lEiT+ah1aK355erdaA3PLh7PP/5Y\nhG2Tk5suy+Q7N4zAZO75jFAATfvzqN7xAbbIFCKSRjN01o+Oeuzh12Q2m2lsbDyp8wshhBBCiMFh\nUIeLNp8ioEKoKKzD6wkw+0fDybt9J45Z4SSGB3hu304afG0MLdpPODD/wTuJiYo6ZpulRc0AeK9P\nZb3fw28SUhlrO/asTv/cW8dX5U3ce85IVq8oYXd+E1fdOZozzz/5R4+0EaCs/VGo0effT2TymCOO\nufXWW8nJyek0dazVaqWtre2kzy+EEEIIIQaPQb3OhV+bMAIW9m+tY8SZybQG/Hy0KNjD0EADNpOZ\ni5KHkPHlTizREccNFgD/+mAf9SNtfDHSxHcjolkSGXPM49v8AX77eTHZcWEkFwfYuq6O7940oleC\nBUDxp4/jqi7EFplyRLBYsmQJSimefvrpjsegmpub0VpLsBBCCCGEECds0PZcaJ8Pn9lCIBCCJ9PE\ntpxW/tFaws6hIUS2wK0jRpAeGkHDpkKqahuJmXT8WZE+XlXG5n1NbL8xjmyrjf9OOPY4C4AXNpdT\n3tzKbyaP4P2Hi5l0ViJzL8046etrqStizyeP4mksIywui+z5v+jYN3/+fD7++ONOx8fEBEOQTCcr\nhBBCCCF6atCGC2rrUBEBvCMtVC2yEGHyMTHfxt+bNWePjiQ9NAKAxk07ABhx0+XHbK7V4+eNV3aR\nf0sipjAzTyRnEGo6dsdQmcPDMxv3MS8zjk3PlBGfYufqu8ac1KrbTeWbaNq/kZa6YlpqdxORNIaM\nqVcTkZjdccyhwSIpKYnq6uoen08IIYQQQohvDNrHooyqKkz4CMRZGLbRxL8NyyVslY+mcBPTDlk8\nz2jzYQ61ETNh1DHbKyxooHhhFI2ZVn6bks4I67EXuVu7r4Elb+RhVoqMPB8el58bfzGO0PCe5b02\nZzWOyi2UrP0TFZtex1GxGVtkCuO+8zizL7oZpRSzZ88GIC4ujszMTLTWEiyEEEIIIUSvGbQ9F237\nqjEFNNSGkDM8FuUxyA/4AGun9S0CbV5MNutx2/ugvpGycyL5jimCiyKij3nsnvoWbvl7AcNjw7jM\nF0XepkquvmsMGcMjenw9Be/eSZsrGBTihs9m7AUPMGTIEPZfd3BtjLVr1wJQX1/f4/MIIYQQQghx\nNIO258J5oBYAHWIhNScWzw4XOzJDiDUphkceDBMBTxvm44SL/T4vb44IEFnu5cHM9GMe+820s2EW\nM/dmDyXvzUpmLkhh1sLUHl2Hp6mcr1+4jDZXNWHxw8m99HeEj74KpRT79+/vOG7mzJkd61UIIYQQ\nQghxKgzanouGphYADJOF2PRw6v5ZTuGQEKYmhHUa81D3xSbChx49MLQZBndUl2MYmvGvN2A/5+jr\nWQC8XlDJhkoH988eyQdPFJOYFsr3lx77katD1Zd8Qe2uf3a89rbU4fM0kpxzIZfc+jQ7Cp/n0Pmp\nzj//fD766KNuty+EEEIIIURPDdpw0dRgACbMWDGZFSU7XdTPMDMj/eCjSdow8Ls8mEOPPn7ioYZq\ntnlbmfn3ZkIbA10eEzA0n5TUsaXKyYq8Ms7MjMXylZuGmlbuemQyVvuxAwlAS10xrto9VG55i1ZH\nBbbIlI59l/ziSxqa1wAQGxtLY2Oj9FIIIYQQQog+N2jDRb0vFGjDYrOhtSbP2QqEMi3x4BgFo80H\nQNy03C7beN/l4NXmRm6IiKUsr4LYuK5DyNMb9vHE+lIAFo1MZGlGKk89t4XZF6UzIvfo62D4Wh1o\nwwBg1/89iKdxHwAJ2ecyeuG9WK1WfD5fp/dceeWV3bl8IYQQQgghet2gDRcusx07bQyfkIC3rJXt\nCSaiUIyMOhgQ/G4PQJc9F8XeNu6prWSaPZRbQ+P5OTB30ZAjjttV5+LpDfu4IDuR/zx7BHHWEB6+\nPY/YRDuXXj/8qPVVbX+f4jW/67QtaewFZE67Fmt4AnfffXenYHHvvffywAMPnOiPQQghhBBCiF4z\naMOF8vjxh4aQkhqBe4uTwiEhTIm2YzpkvEVbTQMAtsS4Tu/1GAa3V5djUyYeT8rAVxf8kG+1BsfH\nP71hH/8sDg4YP+BsI8oWwv1zs4kLtfK/LxZTXe7mtl9NxBZ68MfvrNlFyWdPYBh+ALwtwRmdhp9z\nJwAtLW5Gn3UNhvEzmpubefzxx3nqqaf43e9+x7Jly07Fj0gIIYQQQogTMjjDhT+ANkEg1ILFbqZ0\nexM1I81cP/TgeAt3RQ1lb/0fAPbUhI7tWmvurztAka+NF1KGkBJiYUNxHQBms+Ll/HIeX1fCpJQo\nYuwhJIfbuGFyJnGhVvYXOfn4nf3MWpjK2CkHA4u/zUXhB/fgc9cTO/QMQGENiyM0dj46fgbp6Z0H\nlC9cuJB169bR1tZ2Cn9IQgghhBBCnJhBGi58KD8EYi2ERljYWOeGkVamJ4UDoAMGhb99nqYtu7Al\nxhGWntTx1redTfzN5eD2mATODguGkfyNNTjDYE9YgEc/L+W84fE8uXgcZtPBXpCA3+CV5TuJiLHw\n3ZtGdCrnQMF7+Nz1WMMTyLnotx3bhw0bRmnp0k7Hrlmzhjlz5vT6j0QIIYQQQoiTNTjXufD5UATw\nR1iIsFkosBmEGzAqJji2ouqf62jasovo3JGc/c7vMduD2wvbWvnv+irODA3ntthEAAyteaWmng2T\nLDycV0pOYgS/Oz+nU7AA+N+X9lKx18UVt40iLMLSsd3f5qJs/fMATFjyFBs3biQzMxOAxx57rOO4\nDRs2oLWWYCGEEEIIIQasQdlzYXj9mP0+AuEWAoUedgyxMCnMhrl9vIWnsgaAcb882GvgNALcUVNO\njMnM75PSMSuF1ppfr97DgWQTUw0bt353NFNSowizdJ5adsPqKj55dz/nXJzOhFmJnfYd2PouAGX+\nsZwdebCH5MMPP+Tyyy+XKWWFEEIIIcS3xqAMF75WjdkbIGC3sH+rg6p4M1cdMt7C2+DAEhOJPTke\nCI6z+EVtJft9Xl5JyyLeHPyxPbdpP3/dVklGZYCrpyUxe2jcEefaX+TktT/sYuT4GC7/0chO+9yN\nZbz7l8e4608FwJqO7UopJk6c2PsXLoQQQgghxCk0KMOFPzghE8pu5euiFogzMT0jGC601tSu3YQ1\nNrrj+JebG/ioxclP45KYZg+jqqKFvXsdPLW1lIRGg+xSg8m3Jh1xHmeTlxUPFhARbeHGX+RiDun8\nFFrp2qf42YptHa9NJhNNTU1ERkaegqsWQgghhBDi1BqUYy7a16XDFm4n3/ARFoCxMXYAXEVleBua\nMdutAOS3unm4vppzwyK4KTrYk7Hi8S0sf2UHLQGD1GqDS743nPQhnQNBwG/w/EPbcTl83HzveCKj\ng+3dd999KKVQStFYtp73fzMDm81Gc3MzgUBAgoUQQgghhPjWGpQ9FwbBsRU2m50dqSGMD7EQ0j4A\nu/i54BiI7NuuoDHg586aCpJDLDySmI5Siq/XHqCyvIXAtDDs5gC//88ZZKYfGQjefa6IooImrvvJ\nWDJHRnLzzTfz7LPPHnHc6Dm30tp67Sm8WiGEEEIIIfrGoAwX34yRbjHMVCSa+W56OJUffEbl+5/h\nKq3AZLcSmTuCW2oqqPX7eSM9i2izmX8U1fKr9XvwjDPjDQ0wJyuOoZlRR7S/dlUFn66s4NzLMpk+\nLwWlOs8clRRj5W+/mc2ws24jYdS5fXHJQgghhBBCnHKD8rEo3f5Zf58nmK3OyI7iwP99SUvZAaLH\nDif3npt5ztnIpx4X98QnM94WypaqZn780Q6a/QHMBkxPj+aHUzKPaPurfx3gjT/tptK5lkefD/ZI\nREYGx3MkxUaw7X//g3d/dSap4y8jdcJ3sdijj2hDCCGEEEKIb6PB2XNhAsNioqzFhC1UMy4ulHWl\nlcROGsOEX9/Oek8Lvz+wjwvDo7gqKhaAhz4vwh5QTCrwM//cDK78ztgj2t2wuppH/+td/rX11zR7\nDgTPpTVfvvwjHBWbAWjavxFbZDJZZ97SdxcshBBCCCFEHxiU4QLAH26hTCvG6RB8B2rxNTYTEm6n\n1u/n7poKsixWfpWYilKK/AMONh1o5hyvjWEZVq64YcwR7U0ffyFFJdtoainr2JaVlUXjvq9wVGwm\nLC6LST94LrhDKZQalJ1GQgghhBDiNDY4w4XS+MItHIg0c3FMKPXrtgCQuPBMflxTjtMI8Gx8Bo//\n/GuaHW2sGwKWMDBvbcE+MuaIMRRbvqxlb2lhe7BQ5ObmsG3bNpzVO9n6dnAhvpHn/RxlMh9eiRBC\nCCGEEKeNQRkuFJoWqwWAWWNjqH+iAIC/ZsXzVXMDP9UxfPlcERVlLlyTw6kJ9bLIEsEZc8KYNC24\nnkVCQgL19fUkRg/nynOeZ96Uf6PS+3e+XPc5AI37vqY87xUAknMvJjJpdD9cqRBCCCGEEH1nUIYL\ntMZhtWINaCYk2Vm7voCd00fzdHMDSyJjcDxZQdGuJqLT7HwZ4WdhVgJ/WJyLUoro6Giam5s7moqL\nGElaVgQP/eZuwiJ+irNqB22uWvZvfBlP034ikkYz7Ozb+vFihRBCCCGE6BuDMlwoQ1NvsjK2zYRr\n8w4aYiN4/sYLGGO1cW9sMj/etR2AUdcM5W9r93LrjKHtC9+ZgOA8tskxOcyf8DNyx49l2a8nEhZh\nIeDzsPWdg0Eiacwiss/7WX9cohBCCCGEEH1uUIYLgDqzjSnRdpxVFTy39BIMu40nkjL443/nATB9\nYRovbC7H+cX7bA4J4x8lIYxInk2b38V5k+7ivMXTGDcjntxp8VjtwbEUez/7AwCZM64nfvg5hMak\n99v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xCxYs6Gjrgw8+0HPmzNFaa20Yhp40aZJuaWk56dpFz33z78uJOvwe7c22j2fJkiU6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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure(figsize = (12, 6))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "#Plot the univariate AUC on the training data. Store the AUC\n",
    "\n",
    "#Student put code here\n",
    "feature_auc_dict = {}\n",
    "for col in data.drop('y_buy',1).columns:\n",
    "    feature_auc_dict[col] = plotUnivariateROC(data[col], Y, col)\n",
    "\n",
    "\n",
    "# Put a legend below current axis\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0 + box.height * 0.0 , box.width, box.height * 1])\n",
    "ax.legend(loc = 'upper center', bbox_to_anchor = (0.5, -0.15), fancybox = True, \n",
    "              shadow = True, ncol = 4, prop = {'size':10})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we want to add both of the dictionaries created above into a data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>auc</th>\n",
       "      <th>mi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>buy_freq</th>\n",
       "      <td>0.668931</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buy_interval</th>\n",
       "      <td>0.564296</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>expected_time_buy</th>\n",
       "      <td>0.526401</td>\n",
       "      <td>0.031809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>expected_time_visit</th>\n",
       "      <td>0.594029</td>\n",
       "      <td>0.016068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isbuyer</th>\n",
       "      <td>0.667206</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_buy</th>\n",
       "      <td>0.660148</td>\n",
       "      <td>0.191752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_visit</th>\n",
       "      <td>0.814216</td>\n",
       "      <td>0.466288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multiple_buy</th>\n",
       "      <td>0.564313</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multiple_visit</th>\n",
       "      <td>0.733904</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_checkins</th>\n",
       "      <td>0.574946</td>\n",
       "      <td>0.037052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sv_interval</th>\n",
       "      <td>0.717967</td>\n",
       "      <td>0.063482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uniq_urls</th>\n",
       "      <td>0.588509</td>\n",
       "      <td>0.025836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visit_freq</th>\n",
       "      <td>0.780138</td>\n",
       "      <td>0.167713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          auc        mi\n",
       "buy_freq             0.668931  0.000000\n",
       "buy_interval         0.564296  0.000000\n",
       "expected_time_buy    0.526401  0.031809\n",
       "expected_time_visit  0.594029  0.016068\n",
       "isbuyer              0.667206  0.000000\n",
       "last_buy             0.660148  0.191752\n",
       "last_visit           0.814216  0.466288\n",
       "multiple_buy         0.564313  0.000000\n",
       "multiple_visit       0.733904  0.000000\n",
       "num_checkins         0.574946  0.037052\n",
       "sv_interval          0.717967  0.063482\n",
       "uniq_urls            0.588509  0.025836\n",
       "visit_freq           0.780138  0.167713"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add auc and mi each to a single dataframe\n",
    "df_auc = pd.DataFrame(pd.Series(feature_auc_dict), columns = ['auc'])\n",
    "df_mi = pd.DataFrame(pd.Series(feature_mi_dict), columns = ['mi'])\n",
    "\n",
    "#Now merge the two on the feature name\n",
    "feat_imp_df = df_auc.merge(df_mi, left_index = True, right_index = True)\n",
    "feat_imp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To put the different metrics on the same scale, we'll use pandas rank() method for each feature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1052bfc50>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kKnQprtxV+SGXwCEXh8kikjAqdCmOZyfB/12UPadVuUisVOhSeLmr8u/cBXsdGyaLSIKp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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now create a df that holds the ranks of auc and mi \n",
    "feat_ranks = feat_imp_df.rank(axis = 0, ascending = False)\n",
    "\n",
    "#Plot the two ranks\n",
    "plt.plot(feat_ranks.auc, feat_ranks.mi, '.')\n",
    "#Plot a y=x reference line\n",
    "plt.plot(feat_ranks.auc, feat_ranks.auc,)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do the feature importance metrics above completely agree with each other (in terms of rank order)? If not, why do you suppose?\n",
    "\n",
    "Some discussion points:\n",
    "- A feature can be incredibly predictive but has low coverage. I.e., look at 'multiple_buy.' When it is positive it is highly predictive, but it is only positive in a few cases. This will make the AUC lower (because it is the area under the curve).\n",
    "- The Mutual Information as represented by the feature_importance\\_ attribute of the Decision Tree is technically not Mutual Information between X and Y. Technically, it is the cumulative MI of possible multiple binary splits on the given feature throughout the tree. The [source code](https://github.com/scikit-learn/scikit-learn/blob/a24c8b464d094d2c468a16ea9f8bf8d42d949f84/sklearn/tree/_tree.pyx#L1056-L1092) can show you how this is done. Further, this the cumulative MI within the tree, which is conditional on any prior splits. When two (or more) variables are correlated, the information gained from a redundant feature is often very low. Thus, you can see that a feature might have a high AUC but low MI as computed by the DT learning process.\n",
    "- The right way to do this analysis would be to compute only univariate MI between each X and Y separately (the way we did it for AUC)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['buy_freq', 'last_visit', 'multiple_visit', 'sv_interval', 'visit_freq'],\n",
       " ['last_buy', 'last_visit', 'num_checkins', 'sv_interval', 'visit_freq'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now create lists of top 5 features for both auc and mi\n",
    "top5_auc = list(feat_ranks[(feat_ranks.auc <= 5)].index.values)\n",
    "top5_mi = list(feat_ranks[(feat_ranks.mi <= 5)].index.values)\n",
    "top5_auc, top5_mi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is the conclusive step from all the analysis done above. We want to test which method above can be used to produce the best subset of features. What we'll do is use the top 5 features ranked by both AUC and the decision tree feature importance and compare them against each other with different algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x116a78b70>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6Hi+UlCqOmaeYUqwongLFhZqAX6OtBHYyhm2lzo/Wp/71t2+QM4cMcBLkJg5b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Wt2L9EtDhRSFS7n5IpZVIXLM/8UBwrVPZVR7e677+bb3/42juNwzz338MMf/pDPfvazPPLII9ku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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Now do the following\n",
    "1. Split the data into 80/20 train/test\n",
    "2. For each set of features:\n",
    "- build two decision trees (max_depth in {10, 20}) \n",
    "- build two logistic regression (C in {10**-2, 10**2})\n",
    "- get the auc and log-loss on the test set\n",
    "'''\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "\n",
    "def getLogLoss(Ps, Ys, eps = 10**-6):\n",
    "    return ((Ys == 1) * np.log(Ps + eps) + (Ys == 0) * np.log(1 - Ps + eps)).mean()\n",
    "\n",
    "#Split into train and test randomly without using sklearn package\n",
    "#Note, there are many ways to do this:\n",
    "\n",
    "train_pct = 0.8\n",
    "#1. create an array of n random uniform variables drawn on [0,1] range\n",
    "rand = np.random.rand(data.shape[0])\n",
    "#2. Convert to boolean where True = random number < train_pct\n",
    "rand_filt = (rand < train_pct)\n",
    "\n",
    "#Use the filter to index data\n",
    "\n",
    "train = data[rand_filt]\n",
    "test = data[(rand_filt == False)]\n",
    "\n",
    "\n",
    "fsets = [top5_auc, top5_mi]\n",
    "fset_descr = ['auc', 'mi']\n",
    "mxdepths = [5, 10]\n",
    "Cs = [10**-2, 10**2]\n",
    "\n",
    "\n",
    "#Set up plotting box\n",
    "fig = plt.figure(figsize = (15, 8))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "\n",
    "for i, fset in enumerate(fsets):\n",
    "    descr = fset_descr[i]\n",
    "    #set training and testing data\n",
    "    Y_train = train['y_buy']\n",
    "    X_train = train[fset]\n",
    "    Y_test = test['y_buy']\n",
    "    X_test = test[fset]\n",
    "    \n",
    "    for d in mxdepths:\n",
    "        dt = DecisionTreeClassifier(criterion = 'entropy', max_depth = d)\n",
    "        dt.fit(X_train, Y_train)\n",
    "        preds_dt = dt.predict_proba(X_test)[:, 1]\n",
    "        ll_dt = getLogLoss(preds_dt, Y_test)\n",
    "        \n",
    "        plotUnivariateROC(preds_dt, Y_test, '{}:DT:md={}:(LL={})'.format(descr, d, round(ll_dt, 3)))\n",
    "\n",
    "        \n",
    "    for C in Cs:\n",
    "        lr = LogisticRegression(C = C)\n",
    "        lr.fit(X_train, Y_train)\n",
    "        preds_lr = lr.predict_proba(X_test)[:, 1]\n",
    "        ll_lr = getLogLoss(preds_lr, Y_test)\n",
    "\n",
    "        plotUnivariateROC(preds_lr, Y_test, '{}:LR:C={}:(LL={})'.format(descr, C, round(ll_lr, 3)))\n",
    "\n",
    "    \n",
    "# Put a legend below current axis\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0 + box.height * 0.0 , box.width, box.height * 1])\n",
    "ax.legend(loc = 'upper center', bbox_to_anchor = (0.5, -0.15), fancybox = True, \n",
    "              shadow = True, ncol = 2, prop = {'size':10})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So which feature selection method produced a better model? Any intuition as to why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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